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

doi:10.21203/rs.3.rs-5000893/v1

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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

https://doi.org/10.21203/rs.3.rs-5000893/v1

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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

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.

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: < 12; neither: 12–17; morning: > 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) – < 12h or less, 12-13 h, > 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/m²), 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/m²[22]; ≥27.5 kg/m²[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 <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 > very high correlation”. The p-value of < 0.05 was considered statistically significant.

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
ꭓ²; p	3.159; 0.206
BMI Classification (Overweight)
Non-overweight	38	66.7	119	73.0
Overweight	19	33.3	44	27.0
ꭓ²; p	0.831; 0.362
BMI Classification (Obese)
Non-obese	48	84.2	156	95.7
Obese	9	15.8	7	4.3
ꭓ²; p	8.275; 0.004**
TBF Classification
Normal	34	59.6	112	68.7
High	23	40.4	51	31.3
ꭓ²; p	1.554; 0.213
VFL Classification
Normal	47	82.5	158	96.9
High	10	17.5	5	3.1
ꭓ²; p	13.930; <0.001**
SM Classification
Normal	34	59.6	110	67.5
High	23	40.4	53	32.5
ꭓ²; p	1.147; 0.284
WC Classification
Normal	43	75.4	131	80.4
High	14	24.6	32	19.6
ꭓ²; p	0.621; 0.431
WHR Classification
Normal	35	61.4	132	81.0
High	22	38.6	31	19.0
ꭓ²; p	8.852; 0.003**
WHtR Classification
Normal	35	61.4	128	78.5
High	22	38.6	35	21.5
ꭓ²; 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
ꭓ²; 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
ꭓ²; 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
ꭓ²; 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
ꭓ²; 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
ꭓ²; 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
ꭓ²; 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
ꭓ²; 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
ꭓ²; 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
ꭓ²; 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
ꭓ²; p	1.784; 0.410
Nighttime fasting duration
< 12h	4	8.5	3	2.1
12–13h	9	19.1	29	20.1
> 13h	34	72.3	112	77.8
ꭓ²; 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.
ꭓ²; 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	< 0.001**
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	< 0.001**
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	0.718
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	0.013*
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	< 0.001**
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	0.094
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	< 0.001**
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	< 0.001**
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	< 0.001**
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	< 0.001**
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	0.009**
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	0.508
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	0.955
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)
ꭓ²; 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)	-	-	-
ꭓ²; 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)
ꭓ²; 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
ꭓ²; p	10.469; 0.005**
Weekday Satisfaction Before Dinner (mm)	29.29 ± 17.59	31.93 ± 19.78	39.74 ± 20.52
ꭓ²; 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.

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.

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.

BMI

Body Mass Index

rMEQ

reduced Morningness-Eveningness Questionnaire

Resting Metabolism

SBP

Systolic Blood Pressure

Skeletal Muscle Percentage

TBF

Total Body Fat

VFL

Visceral Fat Level

Waist Circumference

WHR

Waist-Hip Ratio

WHtR

Waist-Height Ratio

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. 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.

Acknowledgements

The authors would like to thank all participants for participating in this study.

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The authors declare no competing interests.

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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

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Abstract

Background

Methods

Results

Demographic, anthropometric, body composition, chrononutrition and chronotype characteristics of participants

Chrononutrition and sleep behaviors of overall participants, between genders, eating jetlag group, and chronotypes

Association between chrononutrition behaviors and adiposity

Relationship between chrononutrition behaviors and chronotypes with appetite sensations

Discussion

Conclusions

Abbreviations

Declarations

References

Additional Declarations

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