The effects of chrononutrition, chronotype and sleep behavior variabilities on adiposity traits and appetite sensations among a sample of urban Malaysian adults: a cross-sectional study

The effects of chrononutrition, chronotype and sleep behavior variabilities on adiposity traits and appetite sensations among a sample of urban Malaysian adults: a cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The effects of chrononutrition, chronotype and sleep behavior variabilities on adiposity traits and appetite sensations among a sample of urban Malaysian adults: a cross-sectional study Yee-How Say, Mimi Shamirah Nordin, Alvin Lai Oon Ng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5000893/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background We investigated the association of chrononutrition (circadian timing of food intake) and sleep behavior (sleep time, wake up time, sleep duration) variabilities, and chronotype with adiposity traits and appetite sensations among a sample of urban Malaysian adults at Sunway City. Methods A total of 220 participants (M/F = 57/163; aged 22.02 ± 5.19), recorded their meal times, dietary intake, and appetite sensations (via Visual Analogue Scale) before and after meals, for two weekdays and one weekend. Sleep behavior was tracked objectively using an activity wristband, while chronotype was assessed by the Morningness-Eveningness Questionnaire. Anthropometrics and body compositions like waist circumference (WC), waist-hip ratio (WHR), body mass index (BMI), total body fat (TBF), visceral fat level (VFL), skeletal muscle percentage (SM) and resting metabolism (RM) were measured. Results Chrononutrition and sleep behaviors did not differ significantly between genders, but overall participants had significantly later breakfast, lunch, eating midpoint, wake up time, sleep duration, lunch and afternoon latencies during weekend, compared to weekdays. Those who belonged to the delay eating jetlag group had significantly higher weekday, but lower weekend eating windows. Larger caloric intake later in the day was significantly associated with lower BMI, TBF and VFL, but higher SM. Interestingly, higher days of skipping breakfast were significantly associated with lower WC, WHR, and RM. Delay lunch and eating jetlag classes were significantly associated with higher WHR and SM, respectively. Delayed morning and afternoon chrononutrition behaviors were associated with higher hunger and eating thoughts, and lower fullness sensations pre- and post-meals. The morning chronotype was associated with lower satisfaction and fullness sensations post-breakfast, but higher same sensations pre-dinner. Conclusions In conclusion, our study found that larger caloric intake later in the day and advanced lunch jetlag led to lower adiposity, which could be due to lower pre- and post-meal hunger sensations associated with healthier chrononutrition behaviors and morning chronotype. However, the notion of breakfast-skipping leading to obesity was not supported. Here, we offered new insights into modern eating and sleeping habits influencing adiposity and appetite sensations. Nutrition & Dietetics Physiology Chrononutrition Meal time Meal variability Chronotype Sleep variability Appetite sensations Adiposity Background In the contemporary urban environment, the intersection of dietary habits and sleep patterns has become a focal point in understanding metabolic health, particularly concerning obesity and appetite regulation [ 1 ]. As urban centers expand and lifestyles evolve, urban adults are experiencing shifts in their daily routines that disrupt their biological rhythms. This disruption raises questions about how such changes impact adiposity—body fat distribution and accumulation—and appetite sensations [ 2 ]. With the rising prevalence of obesity among Malaysians [ 3 ], it is essential to investigate how chrononutrition and sleep behavior variabilities contribute to these health outcomes. This study focuses on understanding these dynamics within a sample of urban Malaysian adults, aiming to shed light on the nuanced effects of modern living conditions on metabolic health. Chrononutrition, an emerging area of research, examines how the timing of food intake influences metabolic processes in relation to the body's circadian rhythms [ 4 ]. The body's internal clock, or circadian rhythm, regulates various physiological processes, including metabolism, sleep-wake cycles, and hormone release [ 5 ]. Disruptions in these rhythms, such as those caused by irregular eating patterns, can lead to metabolic imbalances and contribute to weight gain [ 6 ]. For adults in urban Malaysia, whose lifestyles often involve irregular study/work hours and late-night activities, understanding how the timing of food intake affects their metabolic health is crucial [ 7 , 8 ]. This aspect of chrononutrition explores not only the quantity but also the timing of meals and its impact on body weight and composition. Sleep behavior is another critical factor influencing adiposity and appetite [ 1 ]. The modern urban lifestyle frequently results in irregular sleep patterns, including insufficient sleep, late bedtimes, and frequent interruptions. Such sleep disturbances have been linked to alterations in appetite-regulating hormones, leading to increased hunger and cravings for high-calorie foods [ 9 ]. Furthermore, poor sleep behaviors can exacerbate stress and affect overall well-being, compounding the risk of weight gain [ 2 ]. This study aims to examine how variations in sleep behaviors among urban Malaysians impact their adiposity traits and appetite sensations, providing a comprehensive view of the relationship between sleep and metabolic health. Malaysia, with its rapidly growing urban population particularly in the Greater Kuala Lumpur region, presents a unique context for this research. The country's urbanization has led to lifestyle changes that deviate from traditional practices, potentially affecting dietary [ 10 ] and sleep patterns [ 11 ]. Urban adults, in particular, are navigating a phase of life characterized by increased autonomy, demanding work schedules, and social pressures, which may influence their eating and sleeping behaviors. By focusing on this demographic, the study seeks to understand how the specific urban environment and lifestyle factors contribute to variations in adiposity and appetite. This localized approach allows for a more precise analysis of how contemporary living conditions affect metabolic health in a specific cultural and geographical context. The objectives of this study are fourfold. First, it aims to explore the impact of chrononutrition—specifically, meal timing and frequency—on adiposity traits and appetite sensations among urban Malaysian adults. Second, it seeks to investigate how variations in sleep behavior, including sleep duration, influence these same health outcomes. Third, the study intends to assess the combined effects of chrononutrition and sleep variabilities on adiposity and appetite sensations, providing insights into their interactive roles. By achieving these objectives, the study hopes to contribute valuable knowledge to the field of metabolic health and inform public health strategies tailored to the needs of urban populations in Malaysia. Methods Participant recruitment and ethical approval Participants in this cross-sectional study were recruited from the students and staff of Sunway University and Sunway College, Sunway City, Selangor, Malaysia, from June – December 2022 (without COVID-19 movement restrictions) by convenience sampling, through publicity materials around campus and word-of-mouth. Participants must meet the following inclusion criteria: 1. Malaysian, aged 18 - 50 years; 2. no current major medical condition (e.g., cancer, liver or kidney disease); 3. no history of or current endocrine pathology (Cushing syndrome, pseudohypoparathyroidism, etc.); 4. no history of neurological disorder or injury (e.g. stroke, or seizures; loss of consciousness > 10 minutes); 5. no history of or current serious psychological disorder (i.e., severe depression or anxiety, substance use disorder, psychoses, bipolar disorder); 6. not currently pregnant or breastfeeding; 7. no impaired sensory function (e.g., visually impaired); 8. no physical activity contraindication; 9. not taking any medication that impacts weight and appetite (e.g., mirtazapine, prednisone); 10. no history of syndromic obesity (Prader Willi, Alström, Laurence-Moon Biedle syndrome, etc.). Screening of the inclusion criteria was performed during the participant’s first visit and if eligible, participants were assigned a subject ID. Briefing on how to answer the online questionnaires was performed, clinical, and anthropometric measurements were taken, and activity wristbands (Xiaomi ® Mi Smart Band 5 ® ) were loaned out. Two weeks later, an exit interview was performed where participants returned the activity wristbands, and were given a reimbursement. Using the Raosoft sample size online calculator (http://www.raosoft.com/samplesize.html), a minimum sample size of 195 is needed to achieve a 7% margin of error, 95% confidence level, Sunway University and Sunway College population size of 22,000, and a 50% response distribution. Ethical approval was obtained from the Sunway University Research Ethics Committee (SUREC 2022/008), all participants signed informed consent forms, and the study was conducted in accordance with the Declaration of Helsinki. Sociodemographic and lifestyle factors questionnaire Sociodemographics, i.e. self-identified Malaysian ethnicity (Malay/Chinese/Indian), age, highest education level (primary/secondary/tertiary), marital status (single/married/divorced or widowed) and monthly household income (B40/M40/T20). According to the Department of Statistics Malaysia (2019), monthly household income is defined as total gross income before taxes, received by all members of a household [for students, unemployed or financially-dependent individuals: parents' household income; for employed and financially-independent individuals: the combined (own, spouse's, children's household income)][12]. The B40, M40 and T20 categories were ≤ MYR4,850, 4851-10,960, and ≥ 10,961 (approximately ≤USD1065, 1066 – 2,406, and ≥2407), respectively [12]. Chronotype and sleeping behaviors Chronotype was assessed using the 5-item reduced Horne-Ostberg Morningness-Eveningness (rMEQ) questionnaire [13]. Total scores of the 5-item rMEQ range from 4 to 26, whereby a higher score indicates a morningness chronotype. The same cutoff scores for determining chronotype groups were used as in [13](evening: 17). Sleep behavior data were tracked via the Zepp Life ® app (iOS ® and Android ® ) using the Xiaomi ® Mi Smart Band 5 (SKU: BHR4215GL). The Xiaomi ® Mi Smart Band 5 has a 3-axis accelerometer, a three-axis gyroscope, a heart rate sensor, and a photoplethysmography sensor to measure some biomedical parameters including sleep behaviors. This activity wristband was chosen because it has been validated in a clinical trial (ClinicalTrials.gov NCT04568408), and was found to have an overall 78% accuracy, 89% sensitivity, and 35% specificity compared to the polysomnography (PSG) gold standard [14]. Albeit, we are aware of the limitations of this activity wristband, in the sense that it is more accurate in detecting wake (48%) and light sleep (51%), rather than in identifying deep sleep (34%) and REM sleep (28%) [14]. It also tends to misidentify PSG sleep phases 40% to 70% of the time, misclassifying 46% of the wake and 65% of the REM sleep stages as light sleep [14]. Participants were instructed to wear the wristbands throughout the day and night, during sleep, in either wrist, at approximately a finger width away from their wrist bone and tightness that allows direct skin contact with the back. They were requested to download the Zepp Life ® app, pair the band via Bluetooth, and turn on the “Automatic heart rate monitoring & sleep assistant” setting in the app to enable more precise sleep data monitoring. Participants were asked to report via screen capture of the Zepp Life ® app on the following parameters: fall asleep time, wake up time, duration of light, deep, REM and time awake during sleep, for any two weekdays and one weekend within a week. Participants who failed to wear the wristband throughout the day and night, as detected by the invalid/blank or abnormally short data, were excluded from analysis. An average of the aforementioned durations was calculated between two weekdays (average weekday), and between two weekdays and one weekend (overall average). Total Sleep Period is the summation of light, deep and REM durations. Dietary Records and Chrononutrition Behaviors Participants were instructed to record all foods and beverages consumed for three 24-hour periods, each day starting at 12:00 am and ending at 11:59 pm, for any two weekdays and one weekend within a 7-day week cycle. Specific details that need to be recorded included: time of meal consumed, place consumed (home, campus, name of restaurant, etc.), and the type of eating occasion or meal (breakfast, lunch, dinner, snack, or other), list each food/beverage item consumed, including foods eaten between meals and all drinks, even if it is a non-caloric item like water, coffee, tea, or sugar free gum, specific details, ingredients, preparation, brand name of each food or beverage consumed, and portion sizes of each food or beverage consumed, using the “Food Amounts Booklet” [15]. The amount of calories consumed for breakfast, lunch, and dinner occasions were estimated based on the Malaysian Food Composition Database (https://myfcd.moh.gov.my/myfcdcurrent/) and Singapore Energy and Nutrition Composition of Food Database (https://focos.hpb.gov.sg/eservices/ENCF/). “Breakfast” was defined as recording their first meal before 1200, “lunch” as recording the second meal between 1201 to 1700, and “dinner” as recording the third meal between 1701 to 2359 within a 24-hour day. Meal skipping was defined as non-record of the meals taken at the above times. Based on the dietary and sleep records, the following (average) weekday or weekend chrononutrition behaviors were calculated [16–18]: Breakfast/lunch/dinner jetlag = Breakfast/Lunch/Dinner time on weekends - Breakfast/Lunch/Dinner time on weekdays; Eating midpoint = ([Timing of the last meal - Timing of the first meal]/2) + Timing of the first meal; Eating jetlag = Eating midpoint on weekends - Eating midpoint on weekdays; Eating window = Last eating event before bedtime – First eating event time; Weekly average eating window (weighted mean) = [(2 × eating window on weekdays) + (1 eating window on weekends)]/3; Sleep duration = Wake time – Fall asleep time; Morning latency = First eating event time – Wake time; Lunch latency = Lunch time – First eating event time; Afternoon latency = Last eating event before bedtime – Lunchtime; Evening latency = Fall asleep time - Last eating event before bedtime; Sleep midpoint = Fall asleep time + (Sleep duration/2); Social jetlag = sleep midpoint weekdays – sleep midpoint weekend. Since the NutriNet-Santé study showed that having a later first meal (later than 0900 compared to earlier than 0800) and last meal of the day (later than 2100 compared to earlier than 2000) were associated with a higher risk of cardiovascular outcomes [19], we also determined the frequencies of the timings of first meal - before 0800, 0800-0900, and after 0900; the timings of last meal - before 2000, 2000-2100, and after 2100; and the durations of nighttime fasting (24 h minus the time elapsed between the first and the last meal of the day) – 13 h. Integer values of social, eating, breakfast, lunch, or dinner jetlag were used to evaluate the frequency of the delay or advance of each sleep or meal timing on weekends. Thereby, “advance” in the timing of a sleep/meal was considered if values were lower than -1, “delay” in the timing of a sleep/meal was considered if values higher than +1, and the “maintenance” in the timing of the sleep/meal was considered if values ranged from -1 to +1 [18]. For example, advance in sleep/meal time would be considered as sleeping/eating two hours earlier on weekends while delayed sleep/meal time indicated eating two hours later on weekends. Maintenance meant sleeping/eating at the same time on weekdays and weekends. The day(s) of breakfast skipping was/were extrapolated by multiplying the total number of two weekdays and one weekend where breakfast time(s) was/were not recorded by 2.33. The largest meal is defined as the meal (breakfast, lunch or dinner) in which largest amount of calories are consumed. Measurement of appetite sensations The visual analogue scale (VAS), 100mm in length with words anchored at each end, expressing the most positive and the most negative rating, were used to assess hunger, satiety, fullness, prospective food consumption, desire to eat something fatty, salty, sweet or savory [20]. Participants were instructed to write down the approximate time the meal was consumed, and to rate for at least 3 meals consumed for three 24-hour time periods, each day starting at 12:00 am and ending at 11:59 pm, for any two weekdays and one weekend within a 7-day week cycle. Clinical, anthropometric and body composition measurements Clinical measurements indicative of vascular health namely systolic blood pressure (SBP), diastolic blood pressure (DBP) and pulse rate were taken using an automated blood pressure monitor (HEM-7121, Omron, Japan) after the subjects had rested for 5 min. Height was measured using a wall-mounted stadiometer. Waist and hip circumferences were measured using a stretch-resistant tape that provided a constant 100 g tension, at the midpoint between the lower margin of the least palpable rib and the top of the iliac crest and around the widest portion of the buttocks, respectively [21]. The waist-hip ratio (WHR) and waist-to-height ratio (WHtR) were calculated by dividing waist circumference by hip circumference and height, respectively. A bioimpedance body composition scale (Omron HBF-375) was used to determine weight, body mass index (BMI; kg/m 2 ), total body fat (TBF; %), visceral fat level (VFL; %), subcutaneous fat (SF; %), skeletal muscle percentage (SM; %) and resting metabolism rate (RM; kcal). The cutoff points for overweight, obesity, high TBF, high VFL, high SM, high WC, high WHR and high WHtR are ≥23 kg/m 2 [22]; ≥27.5 kg/m 2 [22]; 20% (men) or 30% (women) [23]; 10% [23]; 35.8% (men) or 28% (women) [23]; 90 cm (men) or 80 cm (women) [22]; 0.90 (men) or 0.85 (women) [21]; and 0.50 [24], respectively. Statistical analysis Statistical analysis of the data was performed using IBM SPSS Statistics for Windows 26.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics for the categorical variables (demographic characteristics) were presented in terms of frequency and percentage. The conformity of the numerical variables to normal distribution was determined by the Kolmogorov-Smirnov test, where p > 0.05 indicates normally-distributed data. Pearson chi-square test was used to test the differences in categorical variables of demographic, adiposity status, chronotype, social jetlag, and chrononutrition behavior classes between genders. Mann–Whitney U test ( U ) was used in the comparison of two independent groups that did not have a normal distribution, while the Kruskal–Wallis test was used in the comparison of more than two groups. Multiple linear regression was conducted to identify the anthropometric and body composition measurements associated with chrononutrition behaviors. All assumptions for multiple linear regression were fulfilled and the models were controlled for sociodemographic factors: gender, ethnicity, age, marital status, highest education level and monthly household income. Examination of the relationships between the scales was determined by the Spearman rank differences’ correlation coefficient. In the interpretation of the correlation coefficient, it was determined as a “very weak correlation, if very high correlation”. The p -value of < 0.05 was considered statistically significant. Results Demographic, anthropometric, body composition, chrononutrition and chronotype characteristics of participants Out of 281 participants recruited for the study, 230 participants have completed the questionnaires in entirety and had all measurements recorded (dropout rate: 18.1%), while 150 had their chronotype recorded. The mean age of the participants was 22.0 ± 5.2 years; age range: 18–49 years; men: women ratio 1: 3.03. The frequency distribution of ethnicity, overweight, high TBF, high SM, high WC, chronotypes, social, breakfast, lunch, dinner, and eating jetlag classes, days of breakfast skipping, largest meal, timing of first meal, timing of last meal, and nighttime fasting duration did not differ between genders (Table 1 ). However, there were significantly more men who were obese, and had high VFL, WHR, and WHtR, than women (Table 1 ). The majority of the participants had neither/evening chronotypes, belonged to maintenance/delay social, breakfast, lunch, dinner, eating jetlag classes, had zero days of skipping breakfast, had their largest meals during lunch or dinner, had their first meal after 0900, had their last meal before 2000, and had > 13 h of nighttime fasting duration (Table 1 ). Table 1 Socio-demographic characteristics, anthropometric and body composition classifications, and chrononutrition classes according to gender Men ( n = 57) Women ( n = 163) Mean Age 22.28 ± 4.67 21.93 ± 5.37 n % n % Ethnicity Malay 5 8.8 27 16.6 Chinese 50 87.7 125 76.7 Indian 2 3.5 11 6.7 ꭓ 2 ; p 3.159; 0.206 BMI Classification (Overweight) Non-overweight 38 66.7 119 73.0 Overweight 19 33.3 44 27.0 ꭓ 2 ; p 0.831; 0.362 BMI Classification (Obese) Non-obese 48 84.2 156 95.7 Obese 9 15.8 7 4.3 ꭓ 2 ; p 8.275; 0.004** TBF Classification Normal 34 59.6 112 68.7 High 23 40.4 51 31.3 ꭓ 2 ; p 1.554; 0.213 VFL Classification Normal 47 82.5 158 96.9 High 10 17.5 5 3.1 ꭓ 2 ; p 13.930; <0.001** SM Classification Normal 34 59.6 110 67.5 High 23 40.4 53 32.5 ꭓ 2 ; p 1.147; 0.284 WC Classification Normal 43 75.4 131 80.4 High 14 24.6 32 19.6 ꭓ 2 ; p 0.621; 0.431 WHR Classification Normal 35 61.4 132 81.0 High 22 38.6 31 19.0 ꭓ 2 ; p 8.852; 0.003** WHtR Classification Normal 35 61.4 128 78.5 High 22 38.6 35 21.5 ꭓ 2 ; p 6.451; 0.011* Chronotype ( N = 150) Morning 10 25.6 19 17.1 Neither 16 41.0 45 40.5 Evening 13 33.3 47 42.3 ꭓ 2 ; p 1.672; 0.433 Social Jetlag Class ( N = 150) Advance 12 30.8 32 28.8 Maintenance 14 35.9 45 40.5 Delay 13 33.3 34 30.6 ꭓ 2 ; p 0.263; 0.877 Breakfast Jetlag Class Advance 15 30.6 33 22.9 Maintenance 21 42.9 60 41.7 Delay 13 26.5 51 35.4 ꭓ 2 ; p 1.753; 0.416 Lunch Jetlag Class Advance 8 14.3 36 22.4 Maintenance 31 55.4 69 42.9 Delay 17 30.4 56 34.8 ꭓ 2 ; p 2.987; 0.225 Dinner Jetlag Class Advance 11 19.3 30 18.4 Maintenance 36 63.2 110 67.5 Delay 10 17.5 23 14.1 ꭓ 2 ; p 0.469; 0.791 Eating Jetlag Class Advance 5 8.8 21 12.9 Maintenance 38 66.7 100 61.3 Delay 14 24.6 42 25.8 ꭓ 2 ; p 0.819; 0.664 Days Skip Breakfast 0 37 64.9 106 65.0 2 6 10.5 30 18.4 5 9 15.8 18 11.0 7 5 8.8 9 5.5 ꭓ 2 ; p 3.079; 0.380 Largest meal Breakfast 10 17.5 23 14.1 Lunch 21 36.8 66 40.5 Dinner 26 45.6 74 45.4 ꭓ 2 ; p 0.475; 0.789 Time of first meal Before 0800 11 22.4 19 13.2 0800–0900 12 24.5 44 30.6 After 0900 26 53.1 81 56.2 ꭓ 2 ; p 2.545; 0.280 Time of last meal Before 2000 47 82.5 122 74.8 2000–2100 9 15.8 33 20.2 After 2100 1 1.8 8 4.9 ꭓ 2 ; p 1.784; 0.410 Nighttime fasting duration 13h 34 72.3 112 77.8 ꭓ 2 ; p 4.149; 0.126 BMI: Body Mass Index; TBF: Total Body Fat; VFL: Visceral Fat Level; SM: Skeletal Muscle Percentage; WC: Waist Circumference; WHR: Waist-Hip Ratio; WHtR: Waist-Height Ratio. ꭓ 2 ; p values by Chi-square analysis * p -value is significant at the 0.05 level (2-tailed). ** p -value is significant at the 0.01 level (2-tailed). Table 1 goes here Chrononutrition and sleep behaviors of overall participants, between genders, eating jetlag group, and chronotypes Table 2 shows that the chrononutrition and sleep behaviors did not differ between genders. However, overall participants had significantly later/longer breakfast time, lunch time, eating midpoint, wake up time, sleep duration, lunch, and afternoon latencies during weekend, compared to weekdays (Table 2 ). On the other hand, the eating window and morning latency were significantly shorter during weekend, compared to weekdays (Table 2 ). The highest discrepancy in meal timing between weekends and weekdays was at breakfast, whereas dinner timing was mostly maintained during weekends (Table 2 ). Concerning eating jet lag, our results revealed that 39.5% of the population studied showed ≥ 1 h of eating jetlag, out of which 16.8% had ≥ 2 h of eating jet lag. Table 2 Chrononutritive behaviors of overall participants and between genders Chrononutrition and sleep behaviors Men ( n = 57) Women ( n = 173) p -value § Overall p -value ¥ Breakfast Weekdays, hh:mm 09:00 ± 01:24 09:00 ± 01:31 0.880 09:00 ± 01:29 < 0.001** Weekend, hh:mm 09:35 ± 02:01 09:41 ± 01:31 0.452 09:40 ± 01:39 Breakfast jetlag, hh:mm 02:53 ± 03:31 02:33 ± 03:05 0.983 02:38 ± 03:12 - Lunch Weekdays, hh:mm 13:02 ± 01:22 13:09 ± 01:16 0.341 13:07 ± 01:17 < 0.001** Weekend, hh:mm 13:46 ± 01:48 13:37 ± 01:40 0.700 13:39 ± 01:42 Lunch jetlag, hh:mm 01:52 ± 02:51 02:00 ± 02:59 0.176 01:58 ± 02:57 - Dinner Weekdays, hh:mm 19:04 ± 01:27 19:17 ± 01:19 0.382 19:14 ± 01:21 0.718 Weekend, hh:mm 18:37 ± 03:25 19:13 ± 01:26 0.433 19:04 ± 02:08 Dinner jetlag, hh:mm 01:37 ± 03:31 01:30 ± 03:11 0.782 01:32 ± 03:16 - Eating midpoint Weekdays, hh:mm 12:06 ± 04:50 12:34 ± 04:31 0.368 12:26 ± 04:36 0.013* Weekend, hh:mm 13:01 ± 02:30 13:18 ± 02:31 0.343 13:13 ± 02:31 Eating jetlag, hh:mm 01:04 ± 01:13 01:09 ± 01:21 0.482 01:08 ± 01:19 - Eating window Weekdays, h:mm 9:25 ± 2:06 9:38 ± 1:56 0.482 9:35 ± 1:58 < 0.001** Weekend, h:mm 8:48 ± 2:41 8:53 ± 2:16 0.936 8:52 ± 2:22 Weekly Average Eating window , h:mm 9:40 ± 2:43 9:25 ± 1:39 0.926 9:29 ± 1:59 - Sleep Time Weekdays, hh:mm 01:18 ± 00:31 01:57 ± 00:16 0.872 01:47 ± 00:20 0.094 Weekend, hh:mm 00:45 ± 00:35 01:48 ± 00:33 0.950 01:32 ± 00:06 Wake Up Time Weekdays, hh:mm 07:52 ± 01:45 08:13 ± 01:41 0.248 08:07 ± 01:42 < 0.001** Weekend, hh:mm 08:25 ± 02:37 09:07 ± 02:06 0.476 08:56 ± 02:15 Sleep duration Weekdays, h:mm 6:34 ± 1:14 6:16 ± 1.25 0.353 6:20 ± 1:22 < 0.001** Weekend, h:mm 7:40 ± 2:02 7:19 ± 2:27 0.423 7:24 ± 2:21 Morning latency Weekdays, h:mm 4:26 ± 3:49 4:01 ± 3:44 0.349 4:07 ± 3:45 < 0.001** Weekend, h:mm 2:05 ± 3:51 2:33 ± 4:12 0.594 2:26 ± 4:06 Lunch latency Weekdays, h:mm 4:02 ± 1:10 4:12 ± 1:18 0.318 4:09 ± 1:16 < 0.001** Weekend, h:mm 6:42 ± 4:05 5:50 ± 3:42 0.094 6:03 ± 3:49 Afternoon latency Weekdays, h:mm 6:17 ± 1:08 6:12 ± 1:19 0.794 6:13 ± 1:17 0.009** Weekend, h:mm 6:40 ± 3:40 6:17 ± 3:15 0.621 6:23 ± 3:21 Evening latency Weekdays, h:mm 15:03 ± 5:57 14:59 ± 6:14 0.869 15:00 ± 6:09 0.508 Weekend, h:mm 15:07 ± 6:42 15:47 ± 6:03 0.871 15:37 ± 6:13 Sleep midpoint Weekdays, h:mm 6:34 ± 3:30 6:58 ± 3:53 0.595 6:52 ± 3:47 0.955 Weekend, h:mm 6:27 ± 4:12 6:34 ± 4:04 0.660 6:32 ± 4:05 Social jetlag, h:mm 2:59 ± 3:08 2:51 ± 3:10 0.642 2:53 ± 3:09 - Values are mean ± SD, § p -values by Mann-Whitney U test; ¥ p -values by Wilcoxon Signed Ranks test * p -value is significant at the 0.05 level (2-tailed). ** p -value is significant at the 0.01 level (2-tailed). Table 2 goes here Those in the delay eating jetlag group had the longest weekday but shortest weekend eating windows (Table 3 ). They also practiced skipping breakfast for the significantly lowest number of days per week. Similarly, those who belonged to the morning chronotype had the significantly longest weekday eating window. In contrast, the evening chronotype had the significantly shortest weekend and weekly average eating windows, and highest number of days per week of skipping breakfast (Table 3 ), and the highest lunch jetlag [02:18 ± 03:07 vs . 01:09 ± 01:41 (morning); p = 0.028]. The largest meal of the day, chronotype, and social jetlag class were all not associated with eating jetlag class or chronotype (Table 3 ). Table 3 Chrononutritive behavior differences between eating jetlag groups and chronotypes. Eating jetlag classes Chronotype Variable Advance ( n = 26) Maintenance ( n = 138) Delay ( n = 56) Morning ( n = 29) Neither ( n = 61) Evening ( n = 60) Weekday Eating Window, hh:mm 08:44 ± 02:19 09:32 ± 01:56 10:04 ± 01:48 10:24 ± 01:44 10:00 ± 01:37 08:50 ± 02:08 p 0.038* 0.001** Weekend Eating Window, hh:mm 08:21 ± 03:11 09:19 ± 02:09 07:59 ± 02:12 08:45 ± 02:30 09:30 ± 02:44 08:13 ± 01:57 p 0.002** 0.008** Weekly Average Eating Window, hh:mm 08:37 ± 02:01 09:28 ± 01:41 09:55 ± 02:29 09:55 ± 01:36 10:04 ± 02:00 08:50 ± 02:12 p 0.079 < 0.001** Skip Breakfast, day/week 2.62 ± 2.67 1.16 ± 2.12 1.38 ± 2.10 0.86 ± 1.827 0.90 ± 1.758 2.38 ± 2.731 p 0.020* 0.001** Largest meal of the day Breakfast 3 (11.5) 23 (16.7) 7 (12.5) 4 (13.8) 10 (16.4) 7 (11.7) Lunch 13 (50.0) 51 (37.0) 23 (41.1) 11 (37.9) 29 (47.5) 16 (26.7) Dinner 10 (38.5) 64 (46.4) 26 (46.4) 14 (48.3) 22 (36.1) 37 (61.7) ꭓ 2 ; p 1.999; 0.736 8.092; 0.088 Chronotype ( N = 150) Morning 2 (11.8) 17 (18.9) 10 (23.3) - - - Neither 8 (47.1) 36 (40.0) 17 (39.5) - - - Evening 7 (41.2) 37 (41.1) 16 (37.2) - - - ꭓ 2 ; p 1.167; 0.884 - - - Social Jetlag Class ( N = 150) Advance 6 (35.3) 29 (32.2) 9 (20.9) 8 (27.6) 24 (39.3) 12 (20.0) Maintenance 7 (41.2) 37 (41.1) 15 (34.9) 10 (34.5) 21 (34.4) 28 (46.7) Delay 4 (23.5) 24 (26.7) 19 (44.2) 11 (37.9) 16 (26.2) 20 (33.3) ꭓ 2 ; p 5.023; 0.285 6.250; 0.181 Values are mean ± SD, p -values by Mann-Whitney U test * p -value is significant at the 0.05 level (2-tailed). ** p -value is significant at the 0.01 level (2-tailed). Table 3 goes here Association between chrononutrition behaviors and adiposity Multiple linear regression analysis controlling for socio-demographics showed that larger caloric intake later in the day was significantly associated with lower BMI, TBF, and VFL, but higher SM (Table 4 ). Interestingly, higher days of skipping breakfast were significantly associated with lower WC, WHR, and RM (Table 4 ). Delay lunch and eating jetlag classes were also significantly associated with higher WHR [β (95% CI) = 0.017 (0.004, 0.031); p = 0.014] and SM [β (95% CI) = 0.988 (0.096, 1.881); p = 0.030], respectively. All other chrononutrition behaviors, including timing of first meal, timing of last meal, and nighttime fasting duration were not associated with adiposity (data not shown). Table 4 Association between selected chrononutritive behaviors with anthropometric and body composition measurements. Chrononutritive behaviors WC WHR WHtR BMI TBF SF VFL SM RM Largest meal β (95% CI) -1.252 (-3.368, 0.864) 0 (-0.14, 0.014) 0 (-0.019, 0.018) -1.072 (-1.947, -0.197) -1.416 (-2.703, -0.129) -0.912 (-2.197, -0.374) -1.010 (-1.793, -0.227) 0.920 (0.161, 1.679) -31.496 (-78.072, 15.081) p 0.244 0.984 0.973 0.017* 0.031* 0.163 0.012* 0.018* 0.183 Days skip breakfast per week β (95% CI) -0.684 (-1.342, -0.027) -0.006 (-0.010, -0.001) 0 (-0.006, 0.005) -0.063 (0.343, 0.217) -0.142 (-0.552, 0.268) -0.128 (-0.534, 0.278) -0.059 (-0.310, 0.192) -0.045 (-0.288, 0.198) -16.825 (-31.264, -2.386) p 0.042* 0.011* 0.910 0.658 0.495 0.534 0.641 0.713 0.023* WC: waist circumference; WHR: waist-hip ratio; WHtR: waist-height ratio; BMI: body mass index; TBF: total body fat; SF: subcutaneous fat; VFL: visceral fat level; SM: skeletal muscle percentage; RM: resting metabolism. Data was analyzed using linear regression models to test associations between chrononutrition behaviors with continuous outcome measures of anthropometric and body composition measurements. The table shows the unstandardized coefficient (β), 95% CI and p -value associated with each predictor variable. Analyses were conducted with socio-demographics: gender, ethnicity, age, marital status, highest education level and monthly household income as covariates. * p -value is significant at the 0.05 level (2-tailed); ** p -value is significant at the 0.01 level (2-tailed). Table 4 goes here Relationship between chrononutrition behaviors and chronotypes with appetite sensations Table 5 shows the variables of appetite sensations that were significantly correlated with chrononutrition behaviors. Delayed morning chrononutrition behaviors were significantly correlated with increased hungriness and eating thoughts, and decreased fullness and salty, savory, or fatty craving before meals. This is evidenced by the results that later weekday and weekend times of breakfast were significantly correlated with decreased fullness, savory, and fatty cravings before breakfast; while increased weekday and weekend morning latencies were significantly correlated with increased hungriness and eating thoughts before breakfast, and with increased satisfaction after breakfast, respectively (Table 5 ). Table 5 Correlation between chrononutritive behaviors with appetite sensations Appetite sensation Chrononutritive behaviors Weekday Time of Breakfast Weekday Hungriness Before Breakfast r s = 0.25; p = 0.005 Weekday Fullness Before Breakfast r s = -0.211; p = 0.019 Weekday Savory Craving Before Breakfast r s = -0.26; p = 0.004 Weekend Time of Breakfast Weekend Fatty Craving Before Breakfast r s = -0.191; p = 0.045 Weekday Time of Lunch Weekday Hungriness After Lunch r s = 0.211; p = 0.012 Weekday Salty Craving After Lunch r s = -0.177; p = 0.036 Weekend Time of Lunch Weekend Satisfaction After Lunch r s = -0.205; p = 0.017 Weekend Fullness After Lunch r s = -0.249; p = 0.003 Weekend Time of Dinner Weekend Hungriness Before Dinner r s = -0.178; p = 0.036 Weekday Morning Latency Weekday Hungriness Before Breakfast r s = 0.193; p = 0.041 Weekday Eating Thoughts Before Breakfast r s = 0.187; p = 0.048 Weekday Salty Craving Before Breakfast r s = -0.311; p = 0.001 Weekday Savory Craving Before Breakfast r s = -0.321; p = 0.001 Weekday Fatty Craving Before Breakfast r s = -0.277; p = 0.003 Weekend Morning Latency Weekend Hungriness After Breakfast r s = -0.222; p = 0.032 Weekend Satisfaction After Breakfast r s = 0.226; p = 0.029 Weekday Lunch Latency Weekday Hungriness Before Lunch r s = 0.466; p = 0.044 Weekday Eating Thoughts Before Lunch r s = 0.477; p = 0.039 Weekend Lunch Latency Weekend Fullness Before Lunch r s = -0.184; p = 0.032 Weekend Afternoon Latency Weekend Hungriness Before Dinner r s = 0.209; p = 0.014 Weekend Satisfaction Before Dinner r s = -0.191; p = 0.025 Weekend Eating Thoughts Before Dinner r s = 0.245; p = 0.004 Weekday Evening Latency Weekday Eating Thoughts After Dinner r s = -0.173; p = 0.042 Breakfast Jetlag Overall Eating Thoughts Before Breakfast r s = -0.309; p = 0.001 Dinner Jetlag Overall Eating Thoughts Before Dinner r s = -0.212; p = 0.012 Only significant results are shown. r s and p -values by partial Spearman rank correlation, controlling for variables: age, gender, ethnicity, monthly household income, education level and marital status. Delayed afternoon chrononutrition behaviors were significantly correlated with increased hungriness and decreased fullness, before and after meal. This is evidenced by the results that later weekday time of lunch was significantly correlated with increased hungriness after lunch; while increased weekday and weekend lunch latencies were significantly correlated with increased hungriness and eating thoughts before lunch, and decreased fullness before lunch, respectively (Table 5 ). However, delayed evening chrononutrition behaviors, such as later weekend time of dinner, increased weekday evening latency, and increased dinner jetlag, were significantly correlated with decreased hungriness before dinner, decreased eating thoughts after dinner, and decreased eating thoughts before dinner, respectively (Table 5 ). Table 5 goes here Chronotype-wise, participants with the morning chronotype had significantly earlier weekday breakfast time, but lower weekday satisfaction before dinner, compared with other chronotypes (Table 6 ). Similarly, multiple linear regression analysis also showed that increased rMEQ scores (indicating morningness) were significantly associated with decreased weekday satisfaction and fullness after breakfast, decreased weekday hungriness before dinner, increased weekday fullness before dinner, increased weekend satisfaction before dinner, and decreased weekend sweet craving after dinner (Table 6 ). These indicate that those with the morning chronotype had lower satisfaction and fullness after breakfast, but lower hungriness and increased satisfaction and fullness before dinner. They also had decreased sweet cravings after dinner. Table 6 Relationship between appetite sensations and chronotypes Appetite Sensations Mann Whitney U test comparison between chronotypes (Mean ± SD) Morning ( n = 29) Neither ( n = 61) Evening ( n = 60) Weekday Time of Breakfast (hh:mm) 08: 26 ± 01:21 09:05 ± 01:29 09:27 ± 01:23 ꭓ 2 ; p 10.469; 0.005** Weekday Satisfaction Before Dinner (mm) 29.29 ± 17.59 31.93 ± 19.78 39.74 ± 20.52 ꭓ 2 ; p 6.863; 0.032* Appetite Sensations Multiple linear regression analysis [β (CI); p ] Weekday Satisfaction After Breakfast -6.38 (-11.278, -1.481); 0.011* Weekday Fullness After Breakfast -6.733 (-11.280, -2.186); 0.004** Weekday Hungriness Before Dinner -4.932 (-9.115, -0.749); 0.021* Weekend Satisfaction Before Dinner 6.202 (1.289, 11.116); 0.014* Weekday Fullness Before Dinner 4.779 (0.311, 9.247) ;0.036* Weekend Sweet Craving After Dinner -9.021 (-16.677, -1.365); 0.021* Only significant results are shown. Mulitple linear regression analysis was conducted with socio-demographics: gender, ethnicity, age, marital status, highest education level and monthly household income as covariates. β : unstandardized coefficient, CI: confidence interval. * p -value is significant at the 0.05 level (2-tailed); ** p -value is significant at the 0.01 level (2-tailed). Table 6 goes here All in all, these results indicate that delayed morning and afternoon chrononutrition behaviors, instead of evening chrononutrition behaviors, were associated with higher hunger and eating thoughts, and lower fullness sensations pre- and post-meals. The morning chronotype was associated with lower satisfaction and fullness sensations post-breakfast, but higher same sensations pre-dinner. Discussion This study aimed to determine the characteristics of chrononutrition and sleep behaviors, including chronotype, among a sample of urban Malaysian adults. We examined the association between chrononutrition and sleep behaviors with adiposity traits and appetite sensations. There is reported variability in the way adults select their preferred meal timings between weekdays and weekends; on working days, their meal times revolve around workday routines, for example, waking up earlier for work commitment [ 25 ]. Our findings showed that overall participants had a significant discrepancy in the breakfast time, lunch time, eating midpoint, eating window, wake up time, sleep duration, morning, lunch, and afternoon latencies during weekend, compared to weekdays. On weekends, later/longer timings were recorded for all the above variables, except for eating window and morning latency. This finding is similar to previous studies that examined the differences between weekdays and weekends [ 25 – 27 ]. Due to the shifts in social obligations during weekends, sleeping duration was extended, and eating duration became narrower. In other words, adults were having their breakfast later which subsequently led to later other meal timings on weekends. The difference in meal timing between work and workfree days can result in the eating jetlag phenomenon, a similar concept to social jetlag. This study observed an around two-hour difference shift in weekday and weekend morning and lunch latencies, but no significant shift was observed for evening latency. Indeed, this was due to significantly later breakfast, lunch and wake up times during weekends compared to weekdays, but maintaining the same dinner time. Compared to other meal timings, adults were more likely to delay their first meal or breakfast [ 17 , 28 ]. In this study, a third of adults reported a delay in both breakfast and lunch meal timings between weekdays and weekend. In investigating the chrononutrition profile of eating jetlaggers versus adults who maintained meal timing, we found that delay eating jetlaggers had the longest weekday eating window, but the shortest weekend eating window. Similar to findings from previous studies [ 7 , 29 ], eating jetlaggers displayed social jetlag characteristics which explains the variability of meal duration between weekdays and weekends. A scoping review [ 30 ] summarizes that, compared with morning chronotypes, evening chronotypes tend to skip breakfast more frequently [ 31 – 33 ], have later mealtimes [ 17 ], and had their largest caloric intakes at later times of the day [ 34 , 35 ]. Indeed, in this study, the evening chronotype displayed shortest weekend and weekly average eating windows, and higher frequency of breakfast skipping and longer lunch jetlag. On weekends, adults have greater flexibility in planning their daily schedules and become their driving factor in maintaining regular eating and sleep timings [ 36 ]. However, there was no association between chronotype and largest caloric intake of the day, similar with another previous study [ 37 ]. Erratic eating times between weekdays and weekends can negatively impact the human metabolic system, especially the adiposity system [ 18 ]. A synchronized first mealtime, independent of working schedules, offers a healthy metabolic response via the circadian rhythm oscillation. In this study, we found that delay lunch and eating jetlag classes were also significantly associated with higher WHR, indicating central adiposity. However, neither breakfast jetlag, dinner jetlag, nor other chrononutrition behaviors were significantly associated with any of the adiposity measures. Similarly, a previous study found positive association of eating jet lag, but not breakfast or dinner jetlag, with BMI [ 17 ]. Another study also found no significant association between eating window duration, social jetlag and body composition [ 38 ]. Maintaining a consistent schedule for the first and last meals of the day may be crucial for regulating metabolism and weight. Research on time-restricted feeding in humans indicates that consuming food within a regular 8–12 hour window can enhance nutrient utilization and promote overall health [ 39 ]. On the role of larger caloric intake later in the day and breakfast skipping on obesity/adiposity, we found conflicting findings with two recent systematic reviews and meta-analyses. We found that larger caloric intake later in the day was significantly associated with lower adiposity measures (BMI, TBF and VFL); while higher days of skipping breakfast were significantly associated with lower adiposity measures (WC, WHR, and RM). In contrast, a review summarizes that eating a larger portion of daily calories earlier in the day rather than in the evening is thought to support weight loss and improve health, possibly because it better aligns with the body's natural circadian rhythms [ 40 ]. Also, a review summarizes that skipping breakfast is associated with, and increases the risk of overweight/obesity [ 41 ]. However, an updated similar review concluded that there is minimal evidence that breakfast skipping might lead to weight gain and the onset of overweight/obesity [ 42 ]. The possible reasons for the discrepancies in results are discussed in the study limitations. Of note, a recent study found that having a later firstmeal (later than 0900 compared to earlier than 0800) and last meal of the day (later than 2100 compared to earlier than 2000) was associated with a higher risk of cardiovascular outcomes [ 19 ]. However, we found no association between these meal timing patterns with any of the adiposity measures. Studies on the effects of chrononutrition behaviors and chronotype on appetite sensations like satisfaction, fullness, and specific cravings for sweet, salty, savory, and fatty foods are scarce. In this study, we found that the morning chronotype was associated with lower satisfaction and fullness sensations post-breakfast, but higher same sensations pre-dinner. They also had decreased sweet craving after dinner. Similarly, a recent study found that those with morning chronotype was found to have higher satisfaction and fullness as well as reduced desires for sweet, salty, savory, and fatty foods during an oral glucose tolerance test (OGTT) [ 43 ]. In contrast, an earlier study found that those with the morning chronotype rated their meal intake in the morning to be more filling than their evening meal, compared with the evening chronotype [ 44 ]. Liking and wanting for high-fat relative to low-fat foods was also found to be lower in morning meal intake relative to evening, but there were no significant differences between chronotypes [ 44 ]. People with an evening chronotype often allocate most of their energy and macronutrient consumption to later in the day. This pattern, combined with unhealthy eating habits and poor dietary choices, can contribute to overweight and obesity [ 45 , 46 ]. Given the various limitations of this study, its findings should be interpreted cautiously. The cross-sectional design of the study prevents the drawing of causal conclusions. With a relatively small sample size and imbalanced age and gender distribution, the participants were predominantly healthy, well-educated, and ethnically homogeneous individuals from an urban area. Therefore, the results may not apply to the broader, multiethnic Malaysian adult population. Additionally, survey responses may be affected by under- or overreporting due to respondent fatigue and recall bias. Next, we did not use the Chrononutrition Profile-Questionnaire (CP-Q) [ 16 ] in our study, which includes five questions on chrononutrition preferences (beginning with “If you were entirely free to plan your day…”), i.e. preferred first and last eating events, preferred morning and evening latency, and preferred eating window. Therefore, we were unable to assess eating misalignment – the discrepancy between preferred and actual timing of food intake [ 25 ]. Nevertheless, we reckon that the 24-hour dietary records and activity wristband used to objectively track the chrononutrition and sleep behaviors are more robust than self-reported measures in CP-Q. Although the Xiaomi® Mi Smart Band 5 is known for its high accuracy and sensitivity, it performed better in individuals without sleep disorders compared to those with sleep disorders, particularly in measuring total sleep time and deep sleep [ 14 ]. Nonetheless, the study employed a popular and affordable clinically validated activity wristband to objectively track sleep quantity and behaviors, and assessed a comprehensive range of chrononutrition behaviors, appetite sensations, and adiposity measures. Conclusions In conclusion, our study found that larger caloric intake later in the day and shorter lunch jetlag led to lower adiposity, which could be due to lower pre- and post-meal hunger sensations associated with better chrononutrition behaviors and morning chronotype. However, the notion of breakfast-skipping leading to obesity was not supported. Here, we offered new insights into modern eating and sleeping habits that influence adiposity and appetite sensations. Abbreviations BMI Body Mass Index rMEQ reduced Morningness-Eveningness Questionnaire RM Resting Metabolism SBP Systolic Blood Pressure SM Skeletal Muscle Percentage TBF Total Body Fat VFL Visceral Fat Level WC Waist Circumference WHR Waist-Hip Ratio WHtR Waist-Height Ratio Declarations Ethics approval and consent to participate This study was approved by the Sunway University Research Ethics Committee (Approval number: SUREC 2022/008). All participants provided written informed consent to participate in this study. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study was supported by the Sunway University Internal Grant Scheme (grant number: GRTIN-IGS-DBS[S]-19-2022). Authors’ contributions YHS and ALON conceived and designed the study. 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Meal and Sleep Timing before and during the COVID-19 Pandemic: A Cross-Sectional Anonymous Survey Study from Sweden. Clocks & Sleep. 2021;3:251–8. Dashti HS, Scheer FAJL, Saxena R, Garaulet M. Timing of Food Intake: Identifying Contributing Factors to Design Effective Interventions. Advances in Nutrition. 2019;10:606–20. Makarem N, Sears DD, St‐Onge M, Zuraikat FM, Gallo LC, Talavera GA, et al. 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. JAHA. 2021;10:e022024. Mota MC, Silva CM, Balieiro LCT, Gonçalves BF, Fahmy WM, Crispim CA. Association between social jetlag food consumption and meal times in patients with obesity-related chronic diseases. Vadiveloo MK, editor. PLoS ONE. 2019;14:e0212126. Phoi YY, Rogers M, Bonham MP, Dorrian J, Coates AM. A scoping review of chronotype and temporal patterns of eating of adults: tools used, findings, and future directions. Nutr Res Rev. 2022;35:112–35. Reutrakul S, Hood MM, Crowley SJ, Morgan MK, Teodori M, Knutson KL. The Relationship Between Breakfast Skipping, Chronotype, and Glycemic Control in Type 2 Diabetes. Chronobiology International. 2014;31:64–71. Silva CM, Mota MC, Miranda MT, Paim SL, Waterhouse J, Crispim CA. Chronotype, social jetlag and sleep debt are associated with dietary intake among Brazilian undergraduate students. Chronobiology International. 2016;33:740–8. Teixeira GP, Mota MC, Crispim CA. Eveningness is associated with skipping breakfast and poor nutritional intake in Brazilian undergraduate students. Chronobiology International. 2018;35:358–67. Reutrakul S, Hood MM, Crowley SJ, Morgan MK, Teodori M, Knutson KL, et al. Chronotype Is Independently Associated With Glycemic Control in Type 2 Diabetes. Diabetes Care. 2013;36:2523–9. Baron KG, Reid KJ, Kern AS, Zee PC. Role of Sleep Timing in Caloric Intake and BMI. Obesity. 2011;19:1374–81. Goheer A, Holzhauer K, Martinez J, Woolf T, Coughlin JW, Martin L, et al. What influences the “when” of eating and sleeping?A qualitative interview study. Appetite. 2021;156:104980. Yazdinezhad A, Student Research Committee. Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran., Askarpour M, Student Research Committee. Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran., Aboushamsia MM, Student Research Committee. Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran., et al. Evaluating the Effect of Chronotype on Meal Timing and Obesity in Iranian Housewives: A Cross-Sectional Study. J Adv Med Biomed Res. 2019;27:31–6. McHill AW, Phillips AJ, Czeisler CA, Keating L, Yee K, Barger LK, et al. Later circadian timing of food intake is associated with increased body fat. The American Journal of Clinical Nutrition. 2017;106:1213–9. Chaix A, Manoogian ENC, Melkani GC, Panda S. Time-Restricted Eating to Prevent and Manage Chronic Metabolic Diseases. Annu Rev Nutr. 2019;39:291–315. Young IE, Poobalan A, Steinbeck K, O’Connor HT, Parker HM. Distribution of energy intake across the day and weight loss: A systematic review and meta‐analysis. Obesity Reviews. 2023;24:e13537. Ma X, Chen Q, Pu Y, Guo M, Jiang Z, Huang W, et al. Skipping breakfast is associated with overweight and obesity: A systematic review and meta-analysis. Obesity Research & Clinical Practice. 2020;14:1–8. Wicherski J, Schlesinger S, Fischer F. Association between Breakfast Skipping and Body Weight—A Systematic Review and Meta-Analysis of Observational Longitudinal Studies. Nutrients. 2021;13:272. Malin SK, Syeda USA, Remchak M-ME, Heiston EM. Early chronotype favors appetite and reduced later day caloric intake among adults with obesity. Chronobiology International. 2024;41:427–38. Beaulieu K, Oustric P, Alkahtani S, Alhussain M, Pedersen H, Quist JS, et al. Impact of Meal Timing and Chronotype on Food Reward and Appetite Control in Young Adults. Nutrients. 2020;12:1506. Mazri FH, Manaf ZA, Shahar S, Mat Ludin AF. The Association between Chronotype and Dietary Pattern among Adults: A Scoping Review. IJERPH. 2019;17:68. Van Der Merwe C, Münch M, Kruger R. Chronotype Differences in Body Composition, Dietary Intake and Eating Behavior Outcomes: A Scoping Systematic Review. Advances in Nutrition. 2022;13:2357–405. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5000893","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":347095666,"identity":"2c54dde1-9637-4662-a391-b25e3a65ec8e","order_by":0,"name":"Yee-How Say","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYHACNhAhh+AfIFKLMelaEhuI1mJwvPnYg49tdenzZ+Qek/jZxiDHdyOBdTMPPi1njqUbzmw7nLvhRl6aZG8bg7HkjQS223i13Mgxk+bddiB3g0SO2Q3eNobEDQS13H//DailLl1+Ro7Zzb9tDPWEtdzgYQNqYU5gAFp3G2hLggEhLZJn0swkZ/47bLjhzBvz3zLnJAxnnnnYdnMOHi18xw8/k/hwpk5evj3H2PBNmY083/HkYzfe4NGicACZx8gmASIbmPA5TL4BhfsHqvUHHi2jYBSMglEw4gAAMFFUfZhSuBsAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-2363-5239","institution":"Sunway University","correspondingAuthor":true,"prefix":"","firstName":"Yee-How","middleName":"","lastName":"Say","suffix":""},{"id":347095667,"identity":"20e165e9-3a59-4bd2-9450-feb367c5173f","order_by":1,"name":"Mimi Shamirah Nordin","email":"","orcid":"","institution":"Sunway University","correspondingAuthor":false,"prefix":"","firstName":"Mimi","middleName":"Shamirah","lastName":"Nordin","suffix":""},{"id":347095668,"identity":"7f492870-6321-4d30-bf4f-3a27f853fe77","order_by":2,"name":"Alvin Lai Oon Ng","email":"","orcid":"https://orcid.org/0000-0002-6973-9466","institution":"Sunway University","correspondingAuthor":false,"prefix":"","firstName":"Alvin","middleName":"Lai Oon","lastName":"Ng","suffix":""}],"badges":[],"createdAt":"2024-08-30 03:39:50","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5000893/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5000893/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63750126,"identity":"4345fb78-9fd3-45f8-ae11-126ab6f94941","added_by":"auto","created_at":"2024-09-02 03:31:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1492205,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5000893/v1/f9950575-f1d5-488e-b6ca-34615f611005.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eThe effects of chrononutrition, chronotype and sleep behavior variabilities on adiposity traits and appetite sensations among a sample of urban Malaysian adults: a cross-sectional study\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eIn the contemporary urban environment, the intersection of dietary habits and sleep patterns has become a focal point in understanding metabolic health, particularly concerning obesity and appetite regulation [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As urban centers expand and lifestyles evolve, urban adults are experiencing shifts in their daily routines that disrupt their biological rhythms. This disruption raises questions about how such changes impact adiposity\u0026mdash;body fat distribution and accumulation\u0026mdash;and appetite sensations [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. With the rising prevalence of obesity among Malaysians [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], it is essential to investigate how chrononutrition and sleep behavior variabilities contribute to these health outcomes. This study focuses on understanding these dynamics within a sample of urban Malaysian adults, aiming to shed light on the nuanced effects of modern living conditions on metabolic health.\u003c/p\u003e \u003cp\u003eChrononutrition, an emerging area of research, examines how the timing of food intake influences metabolic processes in relation to the body's circadian rhythms [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The body's internal clock, or circadian rhythm, regulates various physiological processes, including metabolism, sleep-wake cycles, and hormone release [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Disruptions in these rhythms, such as those caused by irregular eating patterns, can lead to metabolic imbalances and contribute to weight gain [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. For adults in urban Malaysia, whose lifestyles often involve irregular study/work hours and late-night activities, understanding how the timing of food intake affects their metabolic health is crucial [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This aspect of chrononutrition explores not only the quantity but also the timing of meals and its impact on body weight and composition.\u003c/p\u003e \u003cp\u003eSleep behavior is another critical factor influencing adiposity and appetite [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The modern urban lifestyle frequently results in irregular sleep patterns, including insufficient sleep, late bedtimes, and frequent interruptions. Such sleep disturbances have been linked to alterations in appetite-regulating hormones, leading to increased hunger and cravings for high-calorie foods [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Furthermore, poor sleep behaviors can exacerbate stress and affect overall well-being, compounding the risk of weight gain [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This study aims to examine how variations in sleep behaviors among urban Malaysians impact their adiposity traits and appetite sensations, providing a comprehensive view of the relationship between sleep and metabolic health.\u003c/p\u003e \u003cp\u003eMalaysia, with its rapidly growing urban population particularly in the Greater Kuala Lumpur region, presents a unique context for this research. The country's urbanization has led to lifestyle changes that deviate from traditional practices, potentially affecting dietary [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and sleep patterns [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Urban adults, in particular, are navigating a phase of life characterized by increased autonomy, demanding work schedules, and social pressures, which may influence their eating and sleeping behaviors. By focusing on this demographic, the study seeks to understand how the specific urban environment and lifestyle factors contribute to variations in adiposity and appetite. This localized approach allows for a more precise analysis of how contemporary living conditions affect metabolic health in a specific cultural and geographical context.\u003c/p\u003e \u003cp\u003eThe objectives of this study are fourfold. First, it aims to explore the impact of chrononutrition\u0026mdash;specifically, meal timing and frequency\u0026mdash;on adiposity traits and appetite sensations among urban Malaysian adults. Second, it seeks to investigate how variations in sleep behavior, including sleep duration, influence these same health outcomes. Third, the study intends to assess the combined effects of chrononutrition and sleep variabilities on adiposity and appetite sensations, providing insights into their interactive roles. By achieving these objectives, the study hopes to contribute valuable knowledge to the field of metabolic health and inform public health strategies tailored to the needs of urban populations in Malaysia.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eParticipant recruitment and ethical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants in this cross-sectional study were recruited from the students and staff of Sunway University and Sunway College, Sunway City, Selangor, Malaysia, from June – December 2022 (without COVID-19 movement restrictions) by convenience sampling, through publicity materials around campus and word-of-mouth. Participants must meet the following inclusion criteria: 1. Malaysian, aged 18 - 50 years; 2. no current major medical condition (e.g., cancer, liver or kidney disease); 3. no history of or current endocrine pathology (Cushing syndrome, pseudohypoparathyroidism, etc.); 4. no history of neurological disorder or injury (e.g. stroke, or seizures; loss of consciousness \u0026gt; 10 minutes); 5. no history of or current serious psychological disorder (i.e., severe depression or anxiety, substance use disorder, psychoses, bipolar disorder); 6. not currently pregnant or breastfeeding; 7. no impaired sensory function (e.g., visually impaired); 8. no physical activity contraindication; 9. not taking any medication that impacts weight and appetite (e.g., mirtazapine, prednisone); 10. no history of syndromic obesity (Prader Willi, Alström, Laurence-Moon Biedle syndrome, etc.). Screening of the inclusion criteria was performed during the participant’s first visit and if eligible, participants were assigned a subject ID. Briefing on how to answer the online questionnaires was performed, clinical, and anthropometric measurements were taken, and activity wristbands (Xiaomi\u003csup\u003e®\u003c/sup\u003e Mi Smart Band 5\u003csup\u003e®\u003c/sup\u003e) were loaned out. Two weeks later, an exit interview was performed where participants returned the activity wristbands, and were given a reimbursement.\u003c/p\u003e\n\u003cp\u003eUsing the Raosoft sample size online calculator (http://www.raosoft.com/samplesize.html), a minimum sample size of 195 is needed to achieve a 7% margin of error, 95% confidence level, Sunway University and Sunway College population size of 22,000, and a 50% response distribution. \u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the Sunway University Research Ethics Committee (SUREC 2022/008), all participants signed informed consent forms, and the study was conducted in accordance with the Declaration of Helsinki. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSociodemographic and lifestyle factors questionnaire\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSociodemographics, i.e. self-identified Malaysian ethnicity (Malay/Chinese/Indian), age, highest education level (primary/secondary/tertiary), marital status (single/married/divorced or widowed) and monthly household income (B40/M40/T20). According to the Department of Statistics Malaysia (2019), monthly household income is defined as total gross income before taxes, received by all members of a household [for students, unemployed or financially-dependent individuals: parents' household income; for employed and financially-independent individuals: the combined (own, spouse's, children's household income)][12]. The B40, M40 and T20 categories were ≤ MYR4,850, 4851-10,960, and ≥ 10,961 (approximately ≤USD1065, 1066 – 2,406, and ≥2407), respectively [12]. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChronotype and sleeping behaviors \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChronotype was assessed using the 5-item reduced Horne-Ostberg Morningness-Eveningness (rMEQ) questionnaire [13]. Total scores of the 5-item rMEQ range from 4 to 26, whereby a higher score indicates a morningness chronotype. The same cutoff scores for determining chronotype groups were used as in [13](evening: \u0026lt; 12; neither: 12–17; morning: \u0026gt; 17). \u003c/p\u003e\n\u003cp\u003eSleep behavior data were tracked via the Zepp Life\u003csup\u003e®\u003c/sup\u003e app (iOS\u003csup\u003e®\u003c/sup\u003e and Android\u003csup\u003e®\u003c/sup\u003e) using the Xiaomi\u003csup\u003e®\u003c/sup\u003e Mi Smart Band 5 (SKU: BHR4215GL). The Xiaomi\u003csup\u003e®\u003c/sup\u003e Mi Smart Band 5 has a 3-axis accelerometer, a three-axis gyroscope, a heart rate sensor, and a photoplethysmography sensor to measure some biomedical parameters including sleep behaviors. This activity wristband was chosen because it has been validated in a clinical trial (ClinicalTrials.gov NCT04568408), and was found to have an overall 78% accuracy, 89% sensitivity, and 35% specificity compared to the polysomnography (PSG) gold standard [14]. Albeit, we are aware of the limitations of this activity wristband, in the sense that it is more accurate in detecting wake (48%) and light sleep (51%), rather than in identifying deep sleep (34%) and REM sleep (28%) [14]. It also tends to misidentify PSG sleep phases 40% to 70% of the time, misclassifying 46% of the wake and 65% of the REM sleep stages as light sleep [14]. \u003c/p\u003e\n\u003cp\u003eParticipants were instructed to wear the wristbands throughout the day and night, during sleep, in either wrist, at approximately a finger width away from their wrist bone and tightness that allows direct skin contact with the back. They were requested to download the Zepp Life\u003csup\u003e®\u003c/sup\u003e app, pair the band via Bluetooth, and turn on the “Automatic heart rate monitoring \u0026amp; sleep assistant” setting in the app to enable more precise sleep data monitoring. Participants were asked to report via screen capture of the Zepp Life\u003csup\u003e®\u003c/sup\u003e app on the following parameters: fall asleep time, wake up time, duration of light, deep, REM and time awake during sleep, for any two weekdays and one weekend within a week. Participants who failed to wear the wristband throughout the day and night, as detected by the invalid/blank or abnormally short data, were excluded from analysis. An average of the aforementioned durations was calculated between two weekdays (average weekday), and between two weekdays and one weekend (overall average). Total Sleep Period is the summation of light, deep and REM durations. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDietary Records and Chrononutrition Behaviors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants were instructed to record all foods and beverages consumed for three 24-hour periods, each day starting at 12:00 am and ending at 11:59 pm, for any two weekdays and one weekend within a 7-day week cycle. Specific details that need to be recorded included: time of meal consumed, place consumed (home, campus, name of restaurant, etc.), and the type of eating occasion or meal (breakfast, lunch, dinner, snack, or other), list each food/beverage item consumed, including foods eaten between meals and all drinks, even if it is a non-caloric item like water, coffee, tea, or sugar free gum, specific details, ingredients, preparation, brand name of each food or beverage consumed, and portion sizes of each food or beverage consumed, using the “Food Amounts Booklet” [15]. The amount of calories consumed for breakfast, lunch, and dinner occasions were estimated based on the Malaysian Food Composition Database (https://myfcd.moh.gov.my/myfcdcurrent/) and Singapore Energy and Nutrition Composition of Food Database (https://focos.hpb.gov.sg/eservices/ENCF/).\u003c/p\u003e\n\u003cp\u003e“Breakfast” was defined as recording their first meal before 1200, “lunch” as recording the second meal between 1201 to 1700, and “dinner” as recording the third meal between 1701 to 2359 within a 24-hour day. Meal skipping was defined as non-record of the meals taken at the above times. \u003c/p\u003e\n\u003cp\u003eBased on the dietary and sleep records, the following (average) weekday or weekend chrononutrition behaviors were calculated [16–18]: Breakfast/lunch/dinner jetlag = Breakfast/Lunch/Dinner time on weekends - Breakfast/Lunch/Dinner time on weekdays; Eating midpoint = ([Timing of the last meal - Timing of the first meal]/2) + Timing of the first meal; Eating jetlag = Eating midpoint on weekends - Eating midpoint on weekdays; Eating window = Last eating event before bedtime – First eating event time; Weekly average eating window (weighted mean) = [(2 × eating window on weekdays) + (1 eating window on weekends)]/3; Sleep duration = Wake time – Fall asleep time; Morning latency = First eating event time – Wake time; Lunch latency = Lunch time – First eating event time; Afternoon latency = Last eating event before bedtime – Lunchtime; Evening latency = Fall asleep time - Last eating event before bedtime; Sleep midpoint = Fall asleep time + (Sleep duration/2); Social jetlag = sleep midpoint weekdays – sleep midpoint weekend. \u003c/p\u003e\n\u003cp\u003eSince the NutriNet-Santé study showed that having a later first meal (later than 0900 compared to earlier than 0800) and last meal of the day (later than 2100 compared to earlier than 2000) were associated with a higher risk of cardiovascular outcomes [19], we also determined the frequencies of the timings of first meal - before 0800, 0800-0900, and after 0900; the timings of last meal - before 2000, 2000-2100, and after 2100; and the durations of nighttime fasting (24 h minus the time elapsed between the first and the last meal of the day) – \u0026lt; 12h or less, 12-13 h, \u0026gt; 13 h.\u003c/p\u003e\n\u003cp\u003eInteger values of social, eating, breakfast, lunch, or dinner jetlag were used to evaluate the frequency of the delay or advance of each sleep or meal timing on weekends. Thereby, “advance” in the timing of a sleep/meal was considered if values were lower than -1, “delay” in the timing of a sleep/meal was considered if values higher than +1, and the “maintenance” in the timing of the sleep/meal was considered if values ranged from -1 to +1 [18]. For example, advance in sleep/meal time would be considered as sleeping/eating two hours earlier on weekends while delayed sleep/meal time indicated eating two hours later on weekends. Maintenance meant sleeping/eating at the same time on weekdays and weekends. \u003c/p\u003e\n\u003cp\u003eThe day(s) of breakfast skipping was/were extrapolated by multiplying the total number of two weekdays and one weekend where breakfast time(s) was/were not recorded by 2.33. The largest meal is defined as the meal (breakfast, lunch or dinner) in which largest amount of calories are consumed. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurement of appetite sensations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe visual analogue scale (VAS), 100mm in length with words anchored at each end, expressing the most positive and the most negative rating, were used to assess hunger, satiety, fullness, prospective food consumption, desire to eat something fatty, salty, sweet or savory [20]. Participants were instructed to write down the approximate time the meal was consumed, and to rate for at least 3 meals consumed for three 24-hour time periods, each day starting at 12:00 am and ending at 11:59 pm, for any two weekdays and one weekend within a 7-day week cycle. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical, anthropometric and body composition measurements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical measurements indicative of vascular health namely systolic blood pressure (SBP), diastolic blood pressure (DBP) and pulse rate were taken using an automated blood pressure monitor (HEM-7121, Omron, Japan) after the subjects had rested for 5 min. Height was measured using a wall-mounted stadiometer. Waist and hip circumferences were measured using a stretch-resistant tape that provided a constant 100 g tension, at the midpoint between the lower margin of the least palpable rib and the top of the iliac crest and around the widest portion of the buttocks, respectively [21]. The waist-hip ratio (WHR) and waist-to-height ratio (WHtR) were calculated by dividing waist circumference by hip circumference and height, respectively. A bioimpedance body composition scale (Omron HBF-375) was used to determine weight, body mass index (BMI; kg/m\u003csup\u003e2\u003c/sup\u003e), total body fat (TBF; %), visceral fat level (VFL; %), subcutaneous fat (SF; %), skeletal muscle percentage (SM; %) and resting metabolism rate (RM; kcal). The cutoff points for overweight, obesity, high TBF, high VFL, high SM, high WC, high WHR and high WHtR are ≥23 kg/m\u003csup\u003e2 \u003c/sup\u003e[22]; ≥27.5 kg/m\u003csup\u003e2 \u003c/sup\u003e[22]; 20% (men) or 30% (women) [23]; 10% [23]; 35.8% (men) or 28% (women) [23]; 90 cm (men) or 80 cm (women) [22]; 0.90 (men) or 0.85 (women) [21]; and 0.50 [24], respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analysis of the data was performed using IBM SPSS Statistics for Windows 26.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics for the categorical variables (demographic characteristics) were presented in terms of frequency and percentage. The conformity of the numerical variables to normal distribution was determined by the Kolmogorov-Smirnov test, where \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05 indicates normally-distributed data. Pearson chi-square test was used to test the differences in categorical variables of demographic, adiposity status, chronotype, social jetlag, and chrononutrition behavior classes between genders. Mann–Whitney \u003cem\u003eU\u003c/em\u003e test (\u003cem\u003eU\u003c/em\u003e) was used in the comparison of two independent groups that did not have a normal distribution, while the Kruskal–Wallis test was used in the comparison of more than two groups. Multiple linear regression was conducted to identify the anthropometric and body composition measurements associated with chrononutrition behaviors. All assumptions for multiple linear regression were fulfilled and the models were controlled for sociodemographic factors: gender, ethnicity, age, marital status, highest education level and monthly household income. Examination of the relationships between the scales was determined by the Spearman rank differences’ correlation coefficient. In the interpretation of the correlation coefficient, it was determined as a “very weak correlation, if \u0026lt;0.2”, a “weak correlation between 0.2 and 0.4”, a “moderate correlation between 0.4 and 0.6”, a “high correlation between 0.6 and 0.8”, and “0.8 \u0026gt; very high correlation”. The \u003cem\u003ep\u003c/em\u003e-value of \u0026lt; 0.05 was considered statistically significant. \u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDemographic, anthropometric, body composition, chrononutrition and chronotype characteristics of participants\u003c/h2\u003e \u003cp\u003eOut of 281 participants recruited for the study, 230 participants have completed the questionnaires in entirety and had all measurements recorded (dropout rate: 18.1%), while 150 had their chronotype recorded. The mean age of the participants was 22.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2 years; age range: 18\u0026ndash;49 years; men: women ratio 1: 3.03. The frequency distribution of ethnicity, overweight, high TBF, high SM, high WC, chronotypes, social, breakfast, lunch, dinner, and eating jetlag classes, days of breakfast skipping, largest meal, timing of first meal, timing of last meal, and nighttime fasting duration did not differ between genders (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, there were significantly more men who were obese, and had high VFL, WHR, and WHtR, than women (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The majority of the participants had neither/evening chronotypes, belonged to maintenance/delay social, breakfast, lunch, dinner, eating jetlag classes, had zero days of skipping breakfast, had their largest meals during lunch or dinner, had their first meal after 0900, had their last meal before 2000, and had\u0026thinsp;\u0026gt;\u0026thinsp;13 h of nighttime fasting duration (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio-demographic characteristics, anthropometric and body composition classifications, and chrononutrition classes according to gender\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMen (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;57)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eWomen (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;163)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e22.28\u0026thinsp;\u0026plusmn;\u0026thinsp;4.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e21.93\u0026thinsp;\u0026plusmn;\u0026thinsp;5.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEthnicity\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChinese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eꭓ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e3.159; 0.206\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBMI Classification (Overweight)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-overweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eꭓ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e0.831; 0.362\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBMI Classification (Obese)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-obese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eꭓ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e8.275; 0.004**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTBF Classification\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eꭓ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e1.554; 0.213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eVFL Classification\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eꭓ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e13.930; \u0026lt;0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSM Classification\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eꭓ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e1.147; 0.284\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eWC Classification\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eꭓ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e0.621; 0.431\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eWHR Classification\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eꭓ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e8.852; 0.003**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eWHtR Classification\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eꭓ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e6.451; 0.011*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eChronotype\u003c/em\u003e (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMorning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeither\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eꭓ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e1.672; 0.433\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSocial Jetlag Class\u003c/em\u003e (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaintenance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eꭓ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e0.263; 0.877\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBreakfast Jetlag Class\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaintenance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eꭓ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e1.753; 0.416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLunch Jetlag Class\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaintenance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eꭓ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e2.987; 0.225\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDinner Jetlag Class\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaintenance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eꭓ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e0.469; 0.791\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEating Jetlag Class\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaintenance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eꭓ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e0.819; 0.664\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDays Skip Breakfast\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eꭓ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e3.079; 0.380\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLargest meal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreakfast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLunch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDinner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eꭓ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e0.475; 0.789\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTime of first meal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBefore 0800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0800\u0026ndash;0900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAfter 0900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eꭓ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e2.545; 0.280\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTime of last meal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBefore 2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u0026ndash;2100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAfter 2100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eꭓ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e1.784; 0.410\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNighttime fasting duration\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;12h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u0026ndash;13h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;13h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eꭓ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e4.149; 0.126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eBMI: Body Mass Index; TBF: Total Body Fat; VFL: Visceral Fat Level; SM: Skeletal Muscle Percentage; WC: Waist Circumference; WHR: Waist-Hip Ratio; WHtR: Waist-Height Ratio.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eꭓ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e values by Chi-square analysis\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*\u003cem\u003ep\u003c/em\u003e-value is significant at the 0.05 level (2-tailed).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e**\u003cem\u003ep\u003c/em\u003e-value is significant at the 0.01 level (2-tailed).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cem\u003egoes here\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eChrononutrition and sleep behaviors of overall participants, between genders, eating jetlag group, and chronotypes\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that the chrononutrition and sleep behaviors did not differ between genders. However, overall participants had significantly later/longer breakfast time, lunch time, eating midpoint, wake up time, sleep duration, lunch, and afternoon latencies during weekend, compared to weekdays (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). On the other hand, the eating window and morning latency were significantly shorter during weekend, compared to weekdays (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The highest discrepancy in meal timing between weekends and weekdays was at breakfast, whereas dinner timing was mostly maintained during weekends (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Concerning eating jet lag, our results revealed that 39.5% of the population studied showed\u0026thinsp;\u0026ge;\u0026thinsp;1 h of eating jetlag, out of which 16.8% had\u0026thinsp;\u0026ge;\u0026thinsp;2 h of eating jet lag.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChrononutritive behaviors of overall participants and between genders\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChrononutrition and sleep behaviors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMen (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;57)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWomen (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;173)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003csup\u003e\u0026yen;\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBreakfast\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekdays, hh:mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e09:00\u0026thinsp;\u0026plusmn;\u0026thinsp;01:24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e09:00\u0026thinsp;\u0026plusmn;\u0026thinsp;01:31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e09:00\u0026thinsp;\u0026plusmn;\u0026thinsp;01:29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekend, hh:mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e09:35\u0026thinsp;\u0026plusmn;\u0026thinsp;02:01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e09:41\u0026thinsp;\u0026plusmn;\u0026thinsp;01:31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e09:40\u0026thinsp;\u0026plusmn;\u0026thinsp;01:39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreakfast jetlag, hh:mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e02:53\u0026thinsp;\u0026plusmn;\u0026thinsp;03:31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e02:33\u0026thinsp;\u0026plusmn;\u0026thinsp;03:05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e02:38\u0026thinsp;\u0026plusmn;\u0026thinsp;03:12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLunch\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekdays, hh:mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13:02\u0026thinsp;\u0026plusmn;\u0026thinsp;01:22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13:09\u0026thinsp;\u0026plusmn;\u0026thinsp;01:16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13:07\u0026thinsp;\u0026plusmn;\u0026thinsp;01:17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekend, hh:mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13:46\u0026thinsp;\u0026plusmn;\u0026thinsp;01:48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13:37\u0026thinsp;\u0026plusmn;\u0026thinsp;01:40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13:39\u0026thinsp;\u0026plusmn;\u0026thinsp;01:42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLunch jetlag, hh:mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e01:52\u0026thinsp;\u0026plusmn;\u0026thinsp;02:51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e02:00\u0026thinsp;\u0026plusmn;\u0026thinsp;02:59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e01:58\u0026thinsp;\u0026plusmn;\u0026thinsp;02:57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDinner\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekdays, hh:mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19:04\u0026thinsp;\u0026plusmn;\u0026thinsp;01:27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19:17\u0026thinsp;\u0026plusmn;\u0026thinsp;01:19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19:14\u0026thinsp;\u0026plusmn;\u0026thinsp;01:21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekend, hh:mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18:37\u0026thinsp;\u0026plusmn;\u0026thinsp;03:25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19:13\u0026thinsp;\u0026plusmn;\u0026thinsp;01:26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19:04\u0026thinsp;\u0026plusmn;\u0026thinsp;02:08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDinner jetlag, hh:mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e01:37\u0026thinsp;\u0026plusmn;\u0026thinsp;03:31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e01:30\u0026thinsp;\u0026plusmn;\u0026thinsp;03:11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e01:32\u0026thinsp;\u0026plusmn;\u0026thinsp;03:16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEating midpoint\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekdays, hh:mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12:06\u0026thinsp;\u0026plusmn;\u0026thinsp;04:50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12:34\u0026thinsp;\u0026plusmn;\u0026thinsp;04:31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12:26\u0026thinsp;\u0026plusmn;\u0026thinsp;04:36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.013*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekend, hh:mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13:01\u0026thinsp;\u0026plusmn;\u0026thinsp;02:30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13:18\u0026thinsp;\u0026plusmn;\u0026thinsp;02:31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13:13\u0026thinsp;\u0026plusmn;\u0026thinsp;02:31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEating jetlag, hh:mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e01:04\u0026thinsp;\u0026plusmn;\u0026thinsp;01:13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e01:09\u0026thinsp;\u0026plusmn;\u0026thinsp;01:21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e01:08\u0026thinsp;\u0026plusmn;\u0026thinsp;01:19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEating window\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekdays, h:mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9:25\u0026thinsp;\u0026plusmn;\u0026thinsp;2:06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9:38\u0026thinsp;\u0026plusmn;\u0026thinsp;1:56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9:35\u0026thinsp;\u0026plusmn;\u0026thinsp;1:58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekend, h:mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8:48\u0026thinsp;\u0026plusmn;\u0026thinsp;2:41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8:53\u0026thinsp;\u0026plusmn;\u0026thinsp;2:16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8:52\u0026thinsp;\u0026plusmn;\u0026thinsp;2:22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eWeekly Average Eating window\u003c/em\u003e, h:mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9:40\u0026thinsp;\u0026plusmn;\u0026thinsp;2:43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9:25\u0026thinsp;\u0026plusmn;\u0026thinsp;1:39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9:29\u0026thinsp;\u0026plusmn;\u0026thinsp;1:59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSleep Time\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekdays, hh:mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e01:18\u0026thinsp;\u0026plusmn;\u0026thinsp;00:31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e01:57\u0026thinsp;\u0026plusmn;\u0026thinsp;00:16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e01:47\u0026thinsp;\u0026plusmn;\u0026thinsp;00:20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekend, hh:mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e00:45\u0026thinsp;\u0026plusmn;\u0026thinsp;00:35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e01:48\u0026thinsp;\u0026plusmn;\u0026thinsp;00:33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e01:32\u0026thinsp;\u0026plusmn;\u0026thinsp;00:06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eWake Up Time\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekdays, hh:mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e07:52\u0026thinsp;\u0026plusmn;\u0026thinsp;01:45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e08:13\u0026thinsp;\u0026plusmn;\u0026thinsp;01:41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e08:07\u0026thinsp;\u0026plusmn;\u0026thinsp;01:42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekend, hh:mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e08:25\u0026thinsp;\u0026plusmn;\u0026thinsp;02:37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e09:07\u0026thinsp;\u0026plusmn;\u0026thinsp;02:06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e08:56\u0026thinsp;\u0026plusmn;\u0026thinsp;02:15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSleep duration\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekdays, h:mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6:34\u0026thinsp;\u0026plusmn;\u0026thinsp;1:14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6:16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6:20\u0026thinsp;\u0026plusmn;\u0026thinsp;1:22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekend, h:mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7:40\u0026thinsp;\u0026plusmn;\u0026thinsp;2:02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7:19\u0026thinsp;\u0026plusmn;\u0026thinsp;2:27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7:24\u0026thinsp;\u0026plusmn;\u0026thinsp;2:21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMorning latency\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekdays, h:mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4:26\u0026thinsp;\u0026plusmn;\u0026thinsp;3:49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4:01\u0026thinsp;\u0026plusmn;\u0026thinsp;3:44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4:07\u0026thinsp;\u0026plusmn;\u0026thinsp;3:45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekend, h:mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2:05\u0026thinsp;\u0026plusmn;\u0026thinsp;3:51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2:33\u0026thinsp;\u0026plusmn;\u0026thinsp;4:12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2:26\u0026thinsp;\u0026plusmn;\u0026thinsp;4:06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLunch latency\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekdays, h:mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4:02\u0026thinsp;\u0026plusmn;\u0026thinsp;1:10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4:12\u0026thinsp;\u0026plusmn;\u0026thinsp;1:18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4:09\u0026thinsp;\u0026plusmn;\u0026thinsp;1:16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekend, h:mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6:42\u0026thinsp;\u0026plusmn;\u0026thinsp;4:05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5:50\u0026thinsp;\u0026plusmn;\u0026thinsp;3:42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6:03\u0026thinsp;\u0026plusmn;\u0026thinsp;3:49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAfternoon latency\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekdays, h:mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6:17\u0026thinsp;\u0026plusmn;\u0026thinsp;1:08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6:12\u0026thinsp;\u0026plusmn;\u0026thinsp;1:19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6:13\u0026thinsp;\u0026plusmn;\u0026thinsp;1:17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.009**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekend, h:mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6:40\u0026thinsp;\u0026plusmn;\u0026thinsp;3:40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6:17\u0026thinsp;\u0026plusmn;\u0026thinsp;3:15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6:23\u0026thinsp;\u0026plusmn;\u0026thinsp;3:21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEvening latency\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekdays, h:mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15:03\u0026thinsp;\u0026plusmn;\u0026thinsp;5:57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14:59\u0026thinsp;\u0026plusmn;\u0026thinsp;6:14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15:00\u0026thinsp;\u0026plusmn;\u0026thinsp;6:09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekend, h:mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15:07\u0026thinsp;\u0026plusmn;\u0026thinsp;6:42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15:47\u0026thinsp;\u0026plusmn;\u0026thinsp;6:03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15:37\u0026thinsp;\u0026plusmn;\u0026thinsp;6:13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSleep midpoint\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekdays, h:mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6:34\u0026thinsp;\u0026plusmn;\u0026thinsp;3:30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6:58\u0026thinsp;\u0026plusmn;\u0026thinsp;3:53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6:52\u0026thinsp;\u0026plusmn;\u0026thinsp;3:47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekend, h:mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6:27\u0026thinsp;\u0026plusmn;\u0026thinsp;4:12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6:34\u0026thinsp;\u0026plusmn;\u0026thinsp;4:04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6:32\u0026thinsp;\u0026plusmn;\u0026thinsp;4:05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial jetlag, h:mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2:59\u0026thinsp;\u0026plusmn;\u0026thinsp;3:08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2:51\u0026thinsp;\u0026plusmn;\u0026thinsp;3:10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2:53\u0026thinsp;\u0026plusmn;\u0026thinsp;3:09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eValues are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, \u003csup\u003e\u003cb\u003e\u0026sect;\u003c/b\u003e\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e-values by Mann-Whitney \u003cem\u003eU\u003c/em\u003e test; \u003csup\u003e\u003cb\u003e\u0026yen;\u003c/b\u003e\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e-values by Wilcoxon Signed Ranks test\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e*\u003cem\u003ep\u003c/em\u003e-value is significant at the 0.05 level (2-tailed).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e**\u003cem\u003ep\u003c/em\u003e-value is significant at the 0.01 level (2-tailed).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cem\u003egoes here\u003c/em\u003e\u003c/p\u003e \u003cp\u003eThose in the delay eating jetlag group had the longest weekday but shortest weekend eating windows (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). They also practiced skipping breakfast for the significantly lowest number of days per week. Similarly, those who belonged to the morning chronotype had the significantly longest weekday eating window. In contrast, the evening chronotype had the significantly shortest weekend and weekly average eating windows, and highest number of days per week of skipping breakfast (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), and the highest lunch jetlag [02:18\u0026thinsp;\u0026plusmn;\u0026thinsp;03:07 \u003cem\u003evs\u003c/em\u003e. 01:09\u0026thinsp;\u0026plusmn;\u0026thinsp;01:41 (morning); \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028]. The largest meal of the day, chronotype, and social jetlag class were all not associated with eating jetlag class or chronotype (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChrononutritive behavior differences between eating jetlag groups and chronotypes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eEating jetlag classes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eChronotype\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdvance (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;26)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaintenance (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;138)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDelay (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;56)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMorning (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNeither (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEvening (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;60)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekday Eating Window, hh:mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e08:44\u0026thinsp;\u0026plusmn;\u0026thinsp;02:19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e09:32\u0026thinsp;\u0026plusmn;\u0026thinsp;01:56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10:04\u0026thinsp;\u0026plusmn;\u0026thinsp;01:48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10:24\u0026thinsp;\u0026plusmn;\u0026thinsp;01:44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10:00\u0026thinsp;\u0026plusmn;\u0026thinsp;01:37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e08:50\u0026thinsp;\u0026plusmn;\u0026thinsp;02:08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.038*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekend Eating Window, hh:mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e08:21\u0026thinsp;\u0026plusmn;\u0026thinsp;03:11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e09:19\u0026thinsp;\u0026plusmn;\u0026thinsp;02:09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e07:59\u0026thinsp;\u0026plusmn;\u0026thinsp;02:12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e08:45\u0026thinsp;\u0026plusmn;\u0026thinsp;02:30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e09:30\u0026thinsp;\u0026plusmn;\u0026thinsp;02:44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e08:13\u0026thinsp;\u0026plusmn;\u0026thinsp;01:57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.002**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.008**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekly Average Eating Window, hh:mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e08:37\u0026thinsp;\u0026plusmn;\u0026thinsp;02:01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e09:28\u0026thinsp;\u0026plusmn;\u0026thinsp;01:41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e09:55\u0026thinsp;\u0026plusmn;\u0026thinsp;02:29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e09:55\u0026thinsp;\u0026plusmn;\u0026thinsp;01:36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10:04\u0026thinsp;\u0026plusmn;\u0026thinsp;02:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e08:50\u0026thinsp;\u0026plusmn;\u0026thinsp;02:12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkip Breakfast, day/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.62\u0026thinsp;\u0026plusmn;\u0026thinsp;2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.16\u0026thinsp;\u0026plusmn;\u0026thinsp;2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.38\u0026thinsp;\u0026plusmn;\u0026thinsp;2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;1.827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.90\u0026thinsp;\u0026plusmn;\u0026thinsp;1.758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.38\u0026thinsp;\u0026plusmn;\u0026thinsp;2.731\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.020*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLargest meal of the day\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreakfast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 (16.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7 (11.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLunch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (41.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 (37.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29 (47.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16 (26.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDinner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64 (46.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (46.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (48.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22 (36.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37 (61.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eꭓ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e1.999; 0.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e8.092; 0.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eChronotype\u003c/em\u003e (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMorning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (23.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeither\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (47.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (39.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (41.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (37.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eꭓ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e1.167; 0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSocial Jetlag Class\u003c/em\u003e (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (35.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24 (39.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12 (20.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaintenance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (41.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (34.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (34.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21 (34.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28 (46.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (44.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 (37.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16 (26.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20 (33.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eꭓ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e5.023; 0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e6.250; 0.181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eValues are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, \u003cem\u003ep\u003c/em\u003e-values by Mann-Whitney U test\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*\u003cem\u003ep\u003c/em\u003e-value is significant at the 0.05 level (2-tailed).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e**\u003cem\u003ep\u003c/em\u003e-value is significant at the 0.01 level (2-tailed).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cem\u003egoes here\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between chrononutrition behaviors and adiposity\u003c/h2\u003e \u003cp\u003eMultiple linear regression analysis controlling for socio-demographics showed that larger caloric intake later in the day was significantly associated with lower BMI, TBF, and VFL, but higher SM (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Interestingly, higher days of skipping breakfast were significantly associated with lower WC, WHR, and RM (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Delay lunch and eating jetlag classes were also significantly associated with higher WHR [β (95% CI)\u0026thinsp;=\u0026thinsp;0.017 (0.004, 0.031); \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014] and SM [β (95% CI)\u0026thinsp;=\u0026thinsp;0.988 (0.096, 1.881); \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030], respectively. All other chrononutrition behaviors, including timing of first meal, timing of last meal, and nighttime fasting duration were not associated with adiposity (data not shown).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between selected chrononutritive behaviors with anthropometric and body composition measurements.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChrononutritive behaviors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWHtR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTBF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVFL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRM\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLargest meal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.252 (-3.368, 0.864)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (-0.14, 0.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (-0.019, 0.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.072 (-1.947, -0.197)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.416 (-2.703, -0.129)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.912 (-2.197, -0.374)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.010 (-1.793, -0.227)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.920 (0.161, 1.679)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-31.496 (-78.072, 15.081)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.017*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.031*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.012*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.018*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDays skip breakfast per week\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.684 (-1.342, -0.027)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.006 (-0.010, -0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (-0.006, 0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.063 (0.343, 0.217)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.142 (-0.552, 0.268)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.128 (-0.534, 0.278)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.059 (-0.310, 0.192)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.045 (-0.288, 0.198)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-16.825 (-31.264, -2.386)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.042*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.023*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eWC: waist circumference; WHR: waist-hip ratio; WHtR: waist-height ratio; BMI: body mass index; TBF: total body fat; SF: subcutaneous fat; VFL: visceral fat level; SM: skeletal muscle percentage; RM: resting metabolism.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eData was analyzed using linear regression models to test associations between chrononutrition behaviors with continuous outcome measures of anthropometric and body composition measurements. The table shows the unstandardized coefficient (β), 95% CI and \u003cem\u003ep\u003c/em\u003e-value associated with each predictor variable. Analyses were conducted with socio-demographics: gender, ethnicity, age, marital status, highest education level and monthly household income as covariates. *\u003cem\u003ep\u003c/em\u003e-value is significant at the 0.05 level (2-tailed); **\u003cem\u003ep\u003c/em\u003e-value is significant at the 0.01 level (2-tailed).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cem\u003egoes here\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRelationship between chrononutrition behaviors and chronotypes with appetite sensations\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the variables of appetite sensations that were significantly correlated with chrononutrition behaviors. Delayed morning chrononutrition behaviors were significantly correlated with increased hungriness and eating thoughts, and decreased fullness and salty, savory, or fatty craving before meals. This is evidenced by the results that later weekday and weekend times of breakfast were significantly correlated with decreased fullness, savory, and fatty cravings before breakfast; while increased weekday and weekend morning latencies were significantly correlated with increased hungriness and eating thoughts before breakfast, and with increased satisfaction after breakfast, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation between chrononutritive behaviors with appetite sensations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAppetite sensation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChrononutritive behaviors\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eWeekday Time of Breakfast\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekday Hungriness Before Breakfast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = 0.25; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekday Fullness Before Breakfast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = -0.211; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekday Savory Craving Before Breakfast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = -0.26; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eWeekend Time of Breakfast\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekend Fatty Craving Before Breakfast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = -0.191; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eWeekday Time of Lunch\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekday Hungriness After Lunch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = 0.211; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekday Salty Craving After Lunch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = -0.177; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eWeekend Time of Lunch\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekend Satisfaction After Lunch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = -0.205; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekend Fullness After Lunch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = -0.249; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eWeekend Time of Dinner\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekend Hungriness Before Dinner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = -0.178; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eWeekday Morning Latency\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekday Hungriness Before Breakfast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = 0.193; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekday Eating Thoughts Before Breakfast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = 0.187; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekday Salty Craving Before Breakfast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = -0.311; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekday Savory Craving Before Breakfast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = -0.321; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekday Fatty Craving Before Breakfast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = -0.277; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eWeekend Morning Latency\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekend Hungriness After Breakfast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = -0.222; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekend Satisfaction After Breakfast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = 0.226; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eWeekday Lunch Latency\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekday Hungriness Before Lunch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = 0.466; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekday Eating Thoughts Before Lunch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = 0.477; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eWeekend Lunch Latency\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekend Fullness Before Lunch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = -0.184; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eWeekend Afternoon Latency\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekend Hungriness Before Dinner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = 0.209; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekend Satisfaction Before Dinner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = -0.191; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekend Eating Thoughts Before Dinner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = 0.245; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eWeekday Evening Latency\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekday Eating Thoughts After Dinner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003csub\u003es\u003c/sub\u003e = -0.173; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBreakfast Jetlag\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall Eating Thoughts Before Breakfast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003csub\u003es\u003c/sub\u003e = -0.309; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eDinner Jetlag\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall Eating Thoughts Before Dinner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003csub\u003es\u003c/sub\u003e = -0.212; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eOnly significant results are shown.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003ep\u003c/em\u003e-values by partial Spearman rank correlation, controlling for variables: age, gender, ethnicity, monthly household income, education level and marital status.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDelayed afternoon chrononutrition behaviors were significantly correlated with increased hungriness and decreased fullness, before and after meal. This is evidenced by the results that later weekday time of lunch was significantly correlated with increased hungriness after lunch; while increased weekday and weekend lunch latencies were significantly correlated with increased hungriness and eating thoughts before lunch, and decreased fullness before lunch, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, delayed evening chrononutrition behaviors, such as later weekend time of dinner, increased weekday evening latency, and increased dinner jetlag, were significantly correlated with decreased hungriness before dinner, decreased eating thoughts after dinner, and decreased eating thoughts before dinner, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u003cem\u003egoes here\u003c/em\u003e\u003c/p\u003e \u003cp\u003eChronotype-wise, participants with the morning chronotype had significantly earlier weekday breakfast time, but lower weekday satisfaction before dinner, compared with other chronotypes (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Similarly, multiple linear regression analysis also showed that increased rMEQ scores (indicating morningness) were significantly associated with decreased weekday satisfaction and fullness after breakfast, decreased weekday hungriness before dinner, increased weekday fullness before dinner, increased weekend satisfaction before dinner, and decreased weekend sweet craving after dinner (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). These indicate that those with the morning chronotype had lower satisfaction and fullness after breakfast, but lower hungriness and increased satisfaction and fullness before dinner. They also had decreased sweet cravings after dinner.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelationship between appetite sensations and chronotypes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAppetite Sensations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMann Whitney \u003cem\u003eU\u003c/em\u003e test comparison between chronotypes (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMorning (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNeither (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEvening (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;60)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekday Time of Breakfast (hh:mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e08: 26\u0026thinsp;\u0026plusmn;\u0026thinsp;01:21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e09:05\u0026thinsp;\u0026plusmn;\u0026thinsp;01:29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e09:27\u0026thinsp;\u0026plusmn;\u0026thinsp;01:23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eꭓ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e10.469; 0.005**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekday Satisfaction Before Dinner (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.29\u0026thinsp;\u0026plusmn;\u0026thinsp;17.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.93\u0026thinsp;\u0026plusmn;\u0026thinsp;19.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.74\u0026thinsp;\u0026plusmn;\u0026thinsp;20.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eꭓ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e6.863; 0.032*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAppetite Sensations\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMultiple linear regression analysis [β (CI);\u003c/b\u003e \u003cb\u003ep\u003c/b\u003e\u003cb\u003e]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekday Satisfaction After Breakfast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-6.38 (-11.278, -1.481); 0.011*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekday Fullness After Breakfast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-6.733 (-11.280, -2.186); 0.004**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekday Hungriness Before Dinner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-4.932 (-9.115, -0.749); 0.021*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekend Satisfaction Before Dinner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e6.202 (1.289, 11.116); 0.014*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekday Fullness Before Dinner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e4.779 (0.311, 9.247) ;0.036*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekend Sweet Craving After Dinner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-9.021 (-16.677, -1.365); 0.021*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eOnly significant results are shown.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eMulitple linear regression analysis was conducted with socio-demographics: gender, ethnicity, age, marital status, highest education level and monthly household income as covariates. β : unstandardized coefficient, CI: confidence interval. *\u003cem\u003ep\u003c/em\u003e-value is significant at the 0.05 level (2-tailed); **\u003cem\u003ep\u003c/em\u003e-value is significant at the 0.01 level (2-tailed).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e \u003cem\u003egoes here\u003c/em\u003e\u003c/p\u003e \u003cp\u003eAll in all, these results indicate that delayed morning and afternoon chrononutrition behaviors, instead of evening chrononutrition behaviors, were associated with higher hunger and eating thoughts, and lower fullness sensations pre- and post-meals. The morning chronotype was associated with lower satisfaction and fullness sensations post-breakfast, but higher same sensations pre-dinner.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to determine the characteristics of chrononutrition and sleep behaviors, including chronotype, among a sample of urban Malaysian adults. We examined the association between chrononutrition and sleep behaviors with adiposity traits and appetite sensations. There is reported variability in the way adults select their preferred meal timings between weekdays and weekends; on working days, their meal times revolve around workday routines, for example, waking up earlier for work commitment [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Our findings showed that overall participants had a significant discrepancy in the breakfast time, lunch time, eating midpoint, eating window, wake up time, sleep duration, morning, lunch, and afternoon latencies during weekend, compared to weekdays. On weekends, later/longer timings were recorded for all the above variables, except for eating window and morning latency. This finding is similar to previous studies that examined the differences between weekdays and weekends [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Due to the shifts in social obligations during weekends, sleeping duration was extended, and eating duration became narrower. In other words, adults were having their breakfast later which subsequently led to later other meal timings on weekends. The difference in meal timing between work and workfree days can result in the eating jetlag phenomenon, a similar concept to social jetlag. This study observed an around two-hour difference shift in weekday and weekend morning and lunch latencies, but no significant shift was observed for evening latency. Indeed, this was due to significantly later breakfast, lunch and wake up times during weekends compared to weekdays, but maintaining the same dinner time. Compared to other meal timings, adults were more likely to delay their first meal or breakfast [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In this study, a third of adults reported a delay in both breakfast and lunch meal timings between weekdays and weekend.\u003c/p\u003e \u003cp\u003eIn investigating the chrononutrition profile of eating jetlaggers versus adults who maintained meal timing, we found that delay eating jetlaggers had the longest weekday eating window, but the shortest weekend eating window. Similar to findings from previous studies [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], eating jetlaggers displayed social jetlag characteristics which explains the variability of meal duration between weekdays and weekends. A scoping review [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] summarizes that, compared with morning chronotypes, evening chronotypes tend to skip breakfast more frequently [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], have later mealtimes [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and had their largest caloric intakes at later times of the day [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Indeed, in this study, the evening chronotype displayed shortest weekend and weekly average eating windows, and higher frequency of breakfast skipping and longer lunch jetlag. On weekends, adults have greater flexibility in planning their daily schedules and become their driving factor in maintaining regular eating and sleep timings [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. However, there was no association between chronotype and largest caloric intake of the day, similar with another previous study [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eErratic eating times between weekdays and weekends can negatively impact the human metabolic system, especially the adiposity system [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. A synchronized first mealtime, independent of working schedules, offers a healthy metabolic response via the circadian rhythm oscillation. In this study, we found that delay lunch and eating jetlag classes were also significantly associated with higher WHR, indicating central adiposity. However, neither breakfast jetlag, dinner jetlag, nor other chrononutrition behaviors were significantly associated with any of the adiposity measures. Similarly, a previous study found positive association of eating jet lag, but not breakfast or dinner jetlag, with BMI [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Another study also found no significant association between eating window duration, social jetlag and body composition [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Maintaining a consistent schedule for the first and last meals of the day may be crucial for regulating metabolism and weight. Research on time-restricted feeding in humans indicates that consuming food within a regular 8\u0026ndash;12 hour window can enhance nutrient utilization and promote overall health [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOn the role of larger caloric intake later in the day and breakfast skipping on obesity/adiposity, we found conflicting findings with two recent systematic reviews and meta-analyses. We found that larger caloric intake later in the day was significantly associated with lower adiposity measures (BMI, TBF and VFL); while higher days of skipping breakfast were significantly associated with lower adiposity measures (WC, WHR, and RM). In contrast, a review summarizes that eating a larger portion of daily calories earlier in the day rather than in the evening is thought to support weight loss and improve health, possibly because it better aligns with the body's natural circadian rhythms [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Also, a review summarizes that skipping breakfast is associated with, and increases the risk of overweight/obesity [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. However, an updated similar review concluded that there is minimal evidence that breakfast skipping might lead to weight gain and the onset of overweight/obesity [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The possible reasons for the discrepancies in results are discussed in the study limitations.\u003c/p\u003e \u003cp\u003eOf note, a recent study found that having a later firstmeal (later than 0900 compared to earlier than 0800) and last meal of the day (later than 2100 compared to earlier than 2000) was associated with a higher risk of cardiovascular outcomes [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, we found no association between these meal timing patterns with any of the adiposity measures.\u003c/p\u003e \u003cp\u003eStudies on the effects of chrononutrition behaviors and chronotype on appetite sensations like satisfaction, fullness, and specific cravings for sweet, salty, savory, and fatty foods are scarce. In this study, we found that the morning chronotype was associated with lower satisfaction and fullness sensations post-breakfast, but higher same sensations pre-dinner. They also had decreased sweet craving after dinner. Similarly, a recent study found that those with morning chronotype was found to have higher satisfaction and fullness as well as reduced desires for sweet, salty, savory, and fatty foods during an oral glucose tolerance test (OGTT) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. In contrast, an earlier study found that those with the morning chronotype rated their meal intake in the morning to be more filling than their evening meal, compared with the evening chronotype [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Liking and wanting for high-fat relative to low-fat foods was also found to be lower in morning meal intake relative to evening, but there were no significant differences between chronotypes [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. People with an evening chronotype often allocate most of their energy and macronutrient consumption to later in the day. This pattern, combined with unhealthy eating habits and poor dietary choices, can contribute to overweight and obesity [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven the various limitations of this study, its findings should be interpreted cautiously. The cross-sectional design of the study prevents the drawing of causal conclusions. With a relatively small sample size and imbalanced age and gender distribution, the participants were predominantly healthy, well-educated, and ethnically homogeneous individuals from an urban area. Therefore, the results may not apply to the broader, multiethnic Malaysian adult population. Additionally, survey responses may be affected by under- or overreporting due to respondent fatigue and recall bias. Next, we did not use the Chrononutrition Profile-Questionnaire (CP-Q) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] in our study, which includes five questions on chrononutrition preferences (beginning with \u0026ldquo;If you were entirely free to plan your day\u0026hellip;\u0026rdquo;), i.e. preferred first and last eating events, preferred morning and evening latency, and preferred eating window. Therefore, we were unable to assess eating misalignment \u0026ndash; the discrepancy between preferred and actual timing of food intake [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Nevertheless, we reckon that the 24-hour dietary records and activity wristband used to objectively track the chrononutrition and sleep behaviors are more robust than self-reported measures in CP-Q. Although the Xiaomi\u0026reg; Mi Smart Band 5 is known for its high accuracy and sensitivity, it performed better in individuals without sleep disorders compared to those with sleep disorders, particularly in measuring total sleep time and deep sleep [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Nonetheless, the study employed a popular and affordable clinically validated activity wristband to objectively track sleep quantity and behaviors, and assessed a comprehensive range of chrononutrition behaviors, appetite sensations, and adiposity measures.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, our study found that larger caloric intake later in the day and shorter lunch jetlag led to lower adiposity, which could be due to lower pre- and post-meal hunger sensations associated with better chrononutrition behaviors and morning chronotype. However, the notion of breakfast-skipping leading to obesity was not supported. Here, we offered new insights into modern eating and sleeping habits that influence adiposity and appetite sensations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003erMEQ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereduced Morningness-Eveningness Questionnaire\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eResting Metabolism\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSystolic Blood Pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSkeletal Muscle Percentage\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTBF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTotal Body Fat\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVFL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVisceral Fat Level\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWaist Circumference\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWaist-Hip Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHtR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWaist-Height Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Sunway University Research Ethics Committee (Approval number: SUREC 2022/008). All participants provided written informed consent to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Sunway University Internal Grant Scheme (grant number: \u0026nbsp; GRTIN-IGS-DBS[S]-19-2022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYHS and ALON conceived and designed the study. YHS and MSN collected the data. YHS analyzed and interpreted the data. YHS wrote the first draft of the manuscript. All authors contributed to manuscript revision and read and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all participants for participating in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGomes S, Ramalhete C, Ferreira I, Bicho M, Valente A. Sleep Patterns, Eating Behavior and the Risk of Noncommunicable Diseases. Nutrients. 2023;15:2462. \u003c/li\u003e\n\u003cli\u003eChaput J-P, McHill AW, Cox RC, Broussard JL, Dutil C, Da Costa BGG, et al. The role of insufficient sleep and circadian misalignment in obesity. Nat Rev Endocrinol. 2023;19:82\u0026ndash;97. \u003c/li\u003e\n\u003cli\u003eInstitute for Public Health (IPH). 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EXCLI Journal; 21:Doc300; ISSN 1611-2156 [Internet]. 2022 [cited 2024 Aug 29]; Available from: https://www.excli.de/index.php/excli/article/view/4324\u003c/li\u003e\n\u003cli\u003eKaur S, Ng CM, Tang SY, Kok EY. Weight status of working adults: The effects of eating misalignment, chronotype, and eating jetlag during mandatory confinement. Chronobiology International. 2023;40:406\u0026ndash;15. \u003c/li\u003e\n\u003cli\u003eHairudin KF, Mohd Fahmi Teng NI, Juliana N. Adaptation and Validation of the Malay-Chrononutrition Profile Questionnaire to Assess Chrononutrition Behavior of Young Adults in Malaysia. Current Developments in Nutrition. 2023;7:100009. \u003c/li\u003e\n\u003cli\u003eAkhlaghi M, Kohanmoo A. Sleep deprivation in development of obesity, effects on appetite regulation, energy metabolism, and dietary choices. Nutr Res Rev. 2023;1\u0026ndash;21. \u003c/li\u003e\n\u003cli\u003eRamadas A, Tham SM, Lalani SA, Shyam S. 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Int J Obes. 2000;24:38\u0026ndash;48. \u003c/li\u003e\n\u003cli\u003eWHO. Waist Circumference and Waist\u0026ndash;Hip Ratio: Report of a WHO Expert Consultation [Internet]. WHO Expert Consultation; 2011 [cited 2023 Jul 25]. Available from: https://apps.who.int/iris/bitstream/handle/10665/44583/9789241501491_eng.pdf\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Regional Office for the Western Pacific. The Asia-Pacific perspective : redefining obesity and its treatment [Internet]. World Health Organization. Regional Office for the Western Pacific; 2000 [cited 2023 Jul 25]. Available from: https://apps.who.int/iris/bitstream/handle/10665/206936/0957708211_eng.pdf?sequence=1\u0026amp;isAllowed=y\u003c/li\u003e\n\u003cli\u003eOmron. Instruction Manual - Body Composition Monitor Model HBF-375 KaradaScan (TM) [Internet]. [cited 2023 Jul 25]. Available from: https://www.omronhealthcare-ap.com/Content/uploads/products/789b8222779742fe808151a86d9851e4.pdf\u003c/li\u003e\n\u003cli\u003eAshwell M, Hsieh SD. Six reasons why the waist-to-height ratio is a rapid and effective global indicator for health risks of obesity and how its use could simplify the international public health message on obesity. International Journal of Food Sciences and Nutrition. 2005;56:303\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eVeronda AC, Irish LA. An examination of eating misalignment: The discrepancy between preferred and actual timing of food intake. Chronobiology International. 2021;38:557\u0026ndash;64. \u003c/li\u003e\n\u003cli\u003eBenedict C, Brand\u0026atilde;o LEM, Merikanto I, Partinen M, Bjorvatn B, Cedernaes J. Meal and Sleep Timing before and during the COVID-19 Pandemic: A Cross-Sectional Anonymous Survey Study from Sweden. Clocks \u0026amp; Sleep. 2021;3:251\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eDashti HS, Scheer FAJL, Saxena R, Garaulet M. Timing of Food Intake: Identifying Contributing Factors to Design Effective Interventions. Advances in Nutrition. 2019;10:606\u0026ndash;20. \u003c/li\u003e\n\u003cli\u003eMakarem N, Sears DD, St‐Onge M, Zuraikat FM, Gallo LC, Talavera GA, et al. 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. JAHA. 2021;10:e022024. \u003c/li\u003e\n\u003cli\u003eMota MC, Silva CM, Balieiro LCT, Gon\u0026ccedil;alves BF, Fahmy WM, Crispim CA. Association between social jetlag food consumption and meal times in patients with obesity-related chronic diseases. Vadiveloo MK, editor. PLoS ONE. 2019;14:e0212126. \u003c/li\u003e\n\u003cli\u003ePhoi YY, Rogers M, Bonham MP, Dorrian J, Coates AM. A scoping review of chronotype and temporal patterns of eating of adults: tools used, findings, and future directions. Nutr Res Rev. 2022;35:112\u0026ndash;35. \u003c/li\u003e\n\u003cli\u003eReutrakul S, Hood MM, Crowley SJ, Morgan MK, Teodori M, Knutson KL. The Relationship Between Breakfast Skipping, Chronotype, and Glycemic Control in Type 2 Diabetes. Chronobiology International. 2014;31:64\u0026ndash;71. \u003c/li\u003e\n\u003cli\u003eSilva CM, Mota MC, Miranda MT, Paim SL, Waterhouse J, Crispim CA. Chronotype, social jetlag and sleep debt are associated with dietary intake among Brazilian undergraduate students. Chronobiology International. 2016;33:740\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eTeixeira GP, Mota MC, Crispim CA. Eveningness is associated with skipping breakfast and poor nutritional intake in Brazilian undergraduate students. Chronobiology International. 2018;35:358\u0026ndash;67. \u003c/li\u003e\n\u003cli\u003eReutrakul S, Hood MM, Crowley SJ, Morgan MK, Teodori M, Knutson KL, et al. Chronotype Is Independently Associated With Glycemic Control in Type 2 Diabetes. Diabetes Care. 2013;36:2523\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eBaron KG, Reid KJ, Kern AS, Zee PC. Role of Sleep Timing in Caloric Intake and BMI. Obesity. 2011;19:1374\u0026ndash;81. \u003c/li\u003e\n\u003cli\u003eGoheer A, Holzhauer K, Martinez J, Woolf T, Coughlin JW, Martin L, et al. What influences the \u0026ldquo;when\u0026rdquo; of eating and sleeping?A qualitative interview study. Appetite. 2021;156:104980. \u003c/li\u003e\n\u003cli\u003eYazdinezhad A, Student Research Committee. Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran., Askarpour M, Student Research Committee. Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran., Aboushamsia MM, Student Research Committee. Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran., et al. Evaluating the Effect of Chronotype on Meal Timing and Obesity in Iranian Housewives: A Cross-Sectional Study. J Adv Med Biomed Res. 2019;27:31\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eMcHill AW, Phillips AJ, Czeisler CA, Keating L, Yee K, Barger LK, et al. Later circadian timing of food intake is associated with increased body fat. The American Journal of Clinical Nutrition. 2017;106:1213\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eChaix A, Manoogian ENC, Melkani GC, Panda S. Time-Restricted Eating to Prevent and Manage Chronic Metabolic Diseases. Annu Rev Nutr. 2019;39:291\u0026ndash;315. \u003c/li\u003e\n\u003cli\u003eYoung IE, Poobalan A, Steinbeck K, O\u0026rsquo;Connor HT, Parker HM. Distribution of energy intake across the day and weight loss: A systematic review and meta‐analysis. Obesity Reviews. 2023;24:e13537. \u003c/li\u003e\n\u003cli\u003eMa X, Chen Q, Pu Y, Guo M, Jiang Z, Huang W, et al. Skipping breakfast is associated with overweight and obesity: A systematic review and meta-analysis. Obesity Research \u0026amp; Clinical Practice. 2020;14:1\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eWicherski J, Schlesinger S, Fischer F. Association between Breakfast Skipping and Body Weight\u0026mdash;A Systematic Review and Meta-Analysis of Observational Longitudinal Studies. Nutrients. 2021;13:272. \u003c/li\u003e\n\u003cli\u003eMalin SK, Syeda USA, Remchak M-ME, Heiston EM. Early chronotype favors appetite and reduced later day caloric intake among adults with obesity. Chronobiology International. 2024;41:427\u0026ndash;38. \u003c/li\u003e\n\u003cli\u003eBeaulieu K, Oustric P, Alkahtani S, Alhussain M, Pedersen H, Quist JS, et al. Impact of Meal Timing and Chronotype on Food Reward and Appetite Control in Young Adults. Nutrients. 2020;12:1506. \u003c/li\u003e\n\u003cli\u003eMazri FH, Manaf ZA, Shahar S, Mat Ludin AF. The Association between Chronotype and Dietary Pattern among Adults: A Scoping Review. IJERPH. 2019;17:68. \u003c/li\u003e\n\u003cli\u003eVan Der Merwe C, M\u0026uuml;nch M, Kruger R. Chronotype Differences in Body Composition, Dietary Intake and Eating Behavior Outcomes: A Scoping Systematic Review. Advances in Nutrition. 2022;13:2357\u0026ndash;405. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"a194c8d6-d922-4178-a392-cd713faa821e","identifier":"10.13039/501100010798","name":"Sunway University","awardNumber":"GRTIN-IGS-DBS[S]-19-2022","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Sunway University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Chrononutrition, Meal time, Meal variability, Chronotype, Sleep variability, Appetite sensations, Adiposity","lastPublishedDoi":"10.21203/rs.3.rs-5000893/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5000893/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe investigated the association of chrononutrition (circadian timing of food intake) and sleep behavior (sleep time, wake up time, sleep duration) variabilities, and chronotype with adiposity traits and appetite sensations among a sample of urban Malaysian adults at Sunway City.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 220 participants (M/F = 57/163; aged 22.02 ± 5.19), recorded their meal times, dietary intake, and appetite sensations (via Visual Analogue Scale) before and after meals, for two weekdays and one weekend. Sleep behavior was tracked objectively using an activity wristband, while chronotype was assessed by the Morningness-Eveningness Questionnaire. Anthropometrics and body compositions like waist circumference (WC), waist-hip ratio (WHR), body mass index (BMI), total body fat (TBF), visceral fat level (VFL), skeletal muscle percentage (SM) and resting metabolism (RM) were measured.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChrononutrition and sleep behaviors did not differ significantly between genders, but overall participants had significantly later breakfast, lunch, eating midpoint, wake up time, sleep duration, lunch and afternoon latencies during weekend, compared to weekdays. Those who belonged to the delay eating jetlag group had significantly higher weekday, but lower weekend eating windows. Larger caloric intake later in the day was significantly associated with lower BMI, TBF and VFL, but higher SM. Interestingly, higher days of skipping breakfast were significantly associated with lower WC, WHR, and RM. Delay lunch and eating jetlag classes were significantly associated with higher WHR and SM, respectively. Delayed morning and afternoon chrononutrition behaviors were associated with higher hunger and eating thoughts, and lower fullness sensations pre- and post-meals. The morning chronotype was associated with lower satisfaction and fullness sensations post-breakfast, but higher same sensations pre-dinner.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn conclusion, our study found that larger caloric intake later in the day and advanced lunch jetlag led to lower adiposity, which could be due to lower pre- and post-meal hunger sensations associated with healthier chrononutrition behaviors and morning chronotype. However, the notion of breakfast-skipping leading to obesity was not supported. Here, we offered new insights into modern eating and sleeping habits influencing adiposity and appetite sensations.\u003c/p\u003e","manuscriptTitle":"The effects of chrononutrition, chronotype and sleep behavior variabilities on adiposity traits and appetite sensations among a sample of urban Malaysian adults: a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-02 03:23:03","doi":"10.21203/rs.3.rs-5000893/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1d28107b-81e0-46a1-b6ef-ed6d421b4b20","owner":[],"postedDate":"September 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":36817903,"name":"Nutrition \u0026 Dietetics"},{"id":36817904,"name":"Physiology"}],"tags":[],"updatedAt":"2024-09-02T03:23:03+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-02 03:23:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5000893","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5000893","identity":"rs-5000893","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}