
==== Front
Sleep Biol Rhythms
Sleep Biol Rhythms
Sleep and Biological Rhythms
1446-9235
1479-8425
Springer Nature Singapore Singapore

539
10.1007/s41105-024-00539-8
Original Article
Association between social jetlag and weight and fat reduction in dieting
Minabe Kai 1
http://orcid.org/0000-0003-0806-4423
Shimura Akiyoshi sim@tokyo-med.ac.jp

12
Sugiura Ko 34
Hino Hiroko 1
Akatsuka Yusaku 3
Seto Takeshi 5
Yanai Miho 5
Masuya Jiro 1
Tamada Yu 6
Inoue Takeshi 1
1 https://ror.org/00k5j5c86 grid.410793.8 0000 0001 0663 3325 Department of Psychiatry, Tokyo Medical University, 6-7-1 Nishi-Shinjuku, Shinjuku-Ku, Tokyo 160-0023 Japan
2 https://ror.org/00f54p054 grid.168010.e 0000 0004 1936 8956 Department of Psychiatry and Behavioral Sciences, Stanford University, 3165 Porter Drive, Palo Alto, CA 94304 USA
3 Department of Research and Development, Children & Future Co., Ltd, 6-16-4 Okusawa,, Setagaya-Ku, Tokyo 158-0083 Japan
4 https://ror.org/048sx0r50 grid.266436.3 0000 0004 1569 9707 Department of Economics, University of Houston, Houston, TX 77004 USA
5 RIZAP Inc, 8-17-1-36F Nishi-Shinjuku, Shinjuku-Ku, Tokyo 160-0023 Japan
6 https://ror.org/00vpv1x26 grid.411909.4 0000 0004 0621 6603 Department of Psychiatry, Tokyo Medical University Hachioji Medical Center, 1163 Tatemachi, Hachioji-Shi, Tokyo 193-0998 Japan
24 6 2024
24 6 2024
10 2024
22 4 513521
10 5 2024
13 6 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Objectives: Social jetlag (SJL), the discrepancy between an individual’s inherent circadian rhythm and external social schedule, is associated with obesity. This study aimed to investigate whether SJL also influences body weight and body fat loss during dieting. Methods: This was an observational study from 2015 to 2018 with participants who had joined an exercise and nutrition program at a private personal training gym. Data from 11,829 individuals provided by the gym along with their sleep logs were analyzed. Differences in change in body mass index (BMI) and body fat percentage (%body fat) were compared by the degree of SJL. Regression was conducted for the change in BMI and %body fat on SJL, adjusted for gender, age, engagement duration in the program, initial BMI, initial %body fat, chronotype, and dietary intakes. Results: The subjects comprised 3,696 men and 8,133 women with a mean age of 40.4 years. Greater SJL was associated with a lower efficacy of BMI and %body fat reduction. The change in BMI (+ 0.56 / hour: SJL) and %body fat (+ 1.40 / hour: SJL) was associated with SJL after adjusting for each variable including dietary intake. Conclusion: SJL was associated with the effect of exercise and nutrition instruction on BMI and body fat reduction, even after adjustment for covariates related to dietary intake. Maintaining consistent sleep–wake rhythms may be crucial for enhancing the efficacy of weight loss programs.

Supplementary Information

The online version contains supplementary material available at 10.1007/s41105-024-00539-8.

Keywords

Social jetlag
Chronobiology
Diet
Exercise
Nutrition
Obesity
Tokyo Medical UniversityOpen access funding provided by Tokyo Medical University.

issue-copyright-statement© Japanese Society of Sleep Research 2024
==== Body
pmcIntroduction

Obesity has rapidly increased globally over a period of 40 years since 1975, reaching a total of 640 million individuals (260 million men and 375 million women). The proportion of obese individuals has increased during this time to 11% in men (a threefold increase), and 15% in women (a twofold increase) [1]. It is well-known that obesity can lead to diabetes [2], hypertension and renal disease [3], cardiovascular disorders [4], gout [5], lipid abnormalities and fatty liver [6], obstructive sleep apnea syndrome [7], orthopedic disorders [8], menstrual irregularities [9] and pregnancy complications [10]. Additionally, it significantly increases the mortality rate [11]. Therefore, the management of obesity has become a public health issue.

Social jetlag (SJL) [12], refers to the mismatch between societal time and the body’s internal clock. Many individuals experience advances in wakeup times and sleep deprivation owing to societal constraints, such as work, school, and household responsibilities, and wake up using alarm clocks on “workdays”. On the other hand, on “freedays”, individuals experience recovery sleep, as they try to catch up on accumulated sleep deprivation by sleeping for prolonged periods. The mismatch in sleep timing between workdays and freedays leads to SJL. Previous studies [13, 14] have indicated a positive association between a high body-mass index (BMI, BMI of 25 or above is “overweight”) and SJL, suggesting that SJL may be a contributing factor to an individual’s present BMI. Furthermore, SJL has been shown to be associated with metabolic risk factors, such as low levels of high-density lipoprotein cholesterol and high levels of triglycerides and neutral fats [14, 15], as well as having a potential association with metabolic syndrome. In addition, SJL has been reported to potentially increase diurnal cortisol levels [16]. A 2-h SJL has been reported to increase the risk of prediabetes and type 2 diabetes by approximately twofold [17] and to be associated with impaired glucose homeostasis, increased fasting plasma insulin concentrations, and insulin resistance [15], thus affecting glucose tolerance. Other studies have noted a decrease in cardiac autonomic regulation as measured by heart rate variability in individuals with high levels of SJL [18], as well as potential associations with cardiovascular diseases, and depression [19]. Moreover, SJL has been reported to be associated with high levels of occupational stress, suggesting that it may be a social issue as well as a health issue [20].

There are limited data on the impact of SJL on efficacy of weight loss programs for addressing obesity. There is only one previous study to our knowledge that has investigated the influence of chronotype and sleep schedule on weight loss outcomes among participants in a weight loss program, which reported that the instability of sleep schedule (inter-daily stability) did not have an effect [21]. However, this previous study did not measure or assess SJL, so the effects of SJL on weight loss outcomes in individuals seeking to lose weight remain unclear. We therefore hypothesized that SJL, which is thought to have an effect on obesity and metabolism, influences the efficacy of weight loss when obese individuals attempt to lose weight. To test our hypothesis, we investigated the effects of SJL on weight and body fat loss in participants in a relatively large exercise and nutrition instruction program.

Participants and methods

Design

This was an observational study. The target sample size to be analyzed was set to around 20,000. As it was indicated that greater than 2 h of social jetlag resulted in poor health outcomes [22], previous studies revealed that the prevalence of people with SJL greater than 2 h is around 3.3–10% [23, 24]. To detect a weak effect (Cohen’s d = 0.2), 651 samples were required, meaning a parent population of around 6500–19500 is needed. Thus, this study was conducted once a sample size close to this target was collected.

Subjects

The subjects of this study were 18,363 adults who participated in a weight loss program at a Japanese private personal training gym between 2015 and 2018. Participants were also instructed to record their sleep/wake times during the intervention. Of the 18,363 subjects, those who recorded their weight and body fat percentage only once or less were excluded from the analysis, and data from the remaining 11,829 subjects (64.4%) were analyzed. Upon joining the program and before starting their exercise and nutrition regimen, participants provided informed consent that their data might be used for future academic analyses in an anonymous manner. This study was conducted with approval from the Tokyo Medical University Medical Ethics Review Committee (study approval no.: T2018-0041), and anonymous data was provided by the private personal training gym company (RIZAP Inc., Tokyo, Japan).

Diet program

The program targets weight and body fat reduction and comprises twice-weekly, 50-min resistance training sessions and individual face-to-face nutritional guidance. In the initial nutritional session, a dietary intake target is established. This daily caloric intake target is derived by subtracting 500–1000 kcal from the daily estimated calorie consumption, calculated based on the daily basal metabolic rate and physical activity level. Furthermore, the target daily caloric intake never falls below the basal metabolic rate. Participants are also educated about the significance of each nutritional component and the importance of maintaining a balanced intake. The personal training regimen emphasizes weight training and incorporates exercises primarily targeting major muscle groups. Participants engage in these 50-min workouts twice weekly at the gym.

Measurements

Their profile data, including age and gender, and body measurements such as height, weight, estimated body fat mass, and percentage (%body fat) were registered in the data table. The participants recorded their sleep logs daily using the mobile application. Regarding dietary intake, the participants also input food items and quantities for each meal into the application, which are then reviewed by program instructors or nutritionists. The application estimates the nutrient intake levels for each food item, based on which the total nutritional intake for each meal is computed and recorded. Participants' body weight and estimated %body fat are measured during their visits to the exercise gym. The %body fat is determined using a bioelectrical impedance analysis machine, which has high validity and accuracy in estimating human body fat percentage, comparable to air displacement plethysmography [21]. There are no specific requirements or inclusion/exclusion criteria regarding the duration of participation in the program. The period from joining the program to the last data registration is defined as “engagement duration” in this study.

Analysis

SJL, as defined by the Munich ChronoType Questionnaire (MCTQ) [25, 26], is calculated by subtracting the midpoint of sleep on free days (MSF) from the midpoint of sleep on workdays (MSW), and taking the absolute value of the resulting difference (SJL =| MSF – MSW |). Participants were divided into the following 3 groups based on the severity of their SJL: SJL < 1 h, 1 h ≤ SJL < 2 h, and 2 h ≤ SJL. One-way ANOVA followed by a post-hoc test (Tukey HSD) was used to compare the mean differences in SJL and reductions in BMI and %body fat between the groups. Additionally, multivariable regression analysis was performed to adjust for factors such as gender, age, engagement duration, initial BMI, initial %body fat, MSFsc: the indicator of chronotype, calculated from MSF with sleep-debt correction by the method of MCTQ, and dietary intake, to analyze reductions in BMI and %body fat. Statistical analyses were performed using IBM SPSS Statistics version 29 software. The statistical significance level was set at p < 0.05 and the 95% confidence interval, as mentioned in the design section.

Sensitivity analyses

As sensitivity analyses, average sleep duration was also adjusted in sensitivity analyses. When adjusting for MSFsc, its value was converted to a floating-point number before being input into the regression analyses. Regarding the covariates in the multivariable model, gender was assessed on a categorical scale, while the others were assessed on a continuous scale.

Results

Demographics

Table 1 illustrates the characteristics of the study participants. The mean age was 40.4 (SD: ± 10.3) years, and 31.2% of the overall participants were men. The mean engagement duration in the program of the participants was 154.8 ± 168.5 days. The mean initial BMI was 27.0 ± 4.8 and the mean initial %body fat was 33.4% ± 8.1%. The overall mean SJL was 0.4 ± 0.5 h, with 89.2% of the participants in the group with SJL < 1 h, 9.3% in the group with 1 h ≤ SJL < 2 h, and 1.5% in the group with SJL ≥ 2 h. There were no significant differences in initial %body fat and height among the 3 groups. The correlations among variables were shown in Table 2.Table 1 Demographic variables and differences in gender and social jetlag

	Total (n = 11,829)		Gender		SJL (Social jetlag)	
		Male (n = 3696)	Female (n = 8133)	P (T)		SJL < 1 h (n = 10,615)	1 h ≤ SJL < 2 h (n = 1053)	2 h ≤ SJL (n = 161)	P (F)	
Age (years old)	40.4 (10.3)		42.3 (9.3)	39.6 (10.6)	***		40.8 (10.3)	37.6 (10.1)	36.5 (9.9)	***	
Engagement duration (days)	154.8 (168.5)		149.2 (167.5)	157.3 (168.9)	*		159.5 (171.1)	115.3 (138.2)	104.2 (128.8)	***	
Body measurement at the first session										
 Body height (cm)	162.8 (8.2)		171.8 (6.1)	158.8 (5.4)	***		162.9 (8.3)	162.5 (8.2)	162.4 (8.2)		
 Body weight (kg)	72.0 (15.8)		84.4 (15.0)	66.4 (12.6)	***		72.2 (15.8)	71.2 (15.3)	69.3 (13.3)	*	
 BMI (kg/m2)	27.0 (4.8)		28.6 (4.7)	26.3 (4.7)	***		27.1 (4.9)	26.8 (4.7)	26.2 (4.3)	*	
 %Body fat	33.4 (8.1)		27.1 (6.1)	36.3 (7.1)	***		33.4 (8.0)	33.6 (8.0)	32.2 (8.5)		
Body measurement changes after the observation period								
 Δ Body weight / BMI (%)	 − 8.6 (6.8)		 − 9.0 (6.3)	 − 8.4 (7.0)	***		 − 8.8 (6.9)	 − 7.4 (6.2)	 − 5.9 (6.3)	***	
 Δ% Body fat	 − 19.1 (16.4)		 − 21.8 (17.7)	 − 17.8 (15.6)	***		 − 19.5 (16.5)	 − 15.9 (15.0)	 − 12.8 (18.1)	***	
Daily food consumption during the program										
 Total calorific intake (kcal)	1352.1 (209.8)		1473.1 (243.8)	1280.2 (144.9)	***		1352.4 (210.9)	1344.2 (194.8)	1383.7 (233.3)		
 Carbohydrate intake (g)	53.3 (29.5)		54.9 (36.5)	52.4 (24.3)	*		53.5 (29.3)	51.2 (29.2)	55.0 (41.7)		
 Fat intake (g)	81.3 (15.5)		88.6 (17.7)	77.0 (12.2)	***		81.2 (15.6)	82.2 (14.8)	84.2 (14.0)		
 Protein intake (g)	101.7 (20.5)		114.0 (21.5)	94.4 (15.8)	***		101.9 (20.5)	99.9 (20.2)	101.6 (19.9)		
Sleep schedules											
 Sleep onset on workdays	0:07 (1:19)		0:07 (1:20)	0:07 (1:19)			24:05 (1:15)	24:19 (1:32)	25:05 (2:35)	***	
 Wakeup on workdays	6:53 (1:13)		6:51 (1:13)	6:54 (1:13)	*		6:50 (1:09)	7:09 (1:25)	8:14 (2:14)	***	
 Sleep duration on workdays	6:39 (0:54)		6:37 (0:51)	6:39 (0:55)	*		6:38 (0:52)	6:41 (0:57)	7:00 (1:28)	***	
 Sleep onset on freedays	0:08 (1:24)		0:03 (1:25)	0:11 (1:23)	***		0:05 (1:19)	0:33 (1:40)	1:32 (2:53)	***	
 Wakeup on freedays	7:10 (1:17)		7:06 (1:18)	7:11 (1:17)	***		7:04 (1:11)	7:44 (1:33)	9:09 (2:37)	***	
 Sleep duration on freedays	6:54 (1:00)		6:56 (0:59)	6:53 (1:01)	*		6:53 (0:57)	7:03 (1:08)	7:29 (2:26)	***	
 Social jetlag	0:24 (0:33)		0:23 (0:35)	0:24 (0:31)			0:16 (0:15)	1:15 (0:17)	3:15 (1:45)	***	
 MSFsc	3:36 (1:12)		3:31 (1:14)	3:39 (1:11)	***		3:33 (1:08)	4:00 (1:25)	5:02 (2:15)	***	
BMI Body Mass Index. MSFsc Midoint of Sleep on Free-days sleep-corrected. *p < 0.05, **p < 0.01, ***p < 0.001; there was a significantly difference among the group (T-test or one-way ANOVA)

Table 2 Correlation between observed variables

	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	
1. ΔBody weight / BMI	–																		
2. Δ%Body fat	.791**	–																	
3. Age	 − .125**	 − .142**	–																
4. Engagement duration (days)	 − .189**	 − .176**	.142**	–															
5. Initial BMI	 − .378**	 − .175**	 − .016	.064**	–														
6. Initial Body fat	 − .294**	 − .115**	 − .081**	.058**	.623**	–													
7. Total caloric intake	.071**	.059**	.073**	.036*	.136**	 − .241**	–												
8. Carbohydrate intake	.103**	.064**	.163**	.205**	 − .140**	 − .159**	.542**	–											
9. Fat intake	.074**	.092**	 − .030	 − .085**	.173**	 − .139**	.720**	 − .052**	–										
10. Protein intake	 − .091**	 − .100**	.002	 − .058**	.255**	 − .153**	.555**	.038*	.215**	–									
11. Sleep onset on workdays	.045**	.046**	 − .181**	 − .009	.000	.002	.013	 − .013	.045**	 − .025	–								
12. Wakeup on workdays	.073**	.087**	 − .295**	 − .073**	.003	.042**	 − .006	 − .060**	.051**	 − .014	.731**	–							
13. Sleep duration on workdays	.045**	.063**	 − .161**	 − .090**	.009	.049**	 − .024	 − .071**	.016	.013	 − .374**	.349**	–						
14. Sleep onset on freedays	.057**	.064**	 − .202**	 − .018*	.001	.035**	 − .003	 − .016	.035*	 − .043*	.834**	.660**	 − .240**	–					
15. Wakeup on freedays	.069**	.090**	 − .297**	 − .077**	.009	.052**	 − .021	 − .057**	.037*	 − .035*	.636**	.834**	.253**	.700**	–				
16. Sleep duration on freedays	.021*	.039**	 − .121**	 − .079**	.014	.015	 − .024	 − .057**	.004	.012	 − .272**	.197**	.643**	 − .410**	.354**	–			
17. Social jet Lag	.083**	.078**	 − .117**	 − .091**	 − .026**	 − .016	.003	 − .022	.025	 − .004	.104**	.163**	.057**	.172**	.256**	.083**	–		
18. MSFsc	.060**	.071**	 − .259**	 − .047**	 − .002	.049**	 − .018	 − .036*	.033	 − .052**	.765**	.806**	.039**	.830**	.831**	 − .032**	.198**	–	
*p < 0.05; **p < 0.01; Pearson’s correlation coefficient (r)

Figure 1 shows a comparison of the decrease in BMI and %body fat among the 3 groups divided by SJL length (SJL < 1 h, 1 h ≤ SJL < 2 h, and 2 h ≤ SJL). Mean BMI reduction was 8.8% ± 6.9% (SJL < 1 h), 7.4% ± 6.2% (1 h ≤ SJL < 2 h), and 5.9% ± 6.3% (2 h ≤ SJL). Mean percent reduction in body fat was 19.5% ± 16.5% (SJL < 1 h), 15.9% ± 15.0% (1 h ≤ SJL < 2 h), and 12.8% ± 18.1% (2 h ≤ SJL). The results of one-way ANOVA and post-hoc analysis (Tukey HSD) demonstrated a significant trend towards a decreasing rate of BMI and body fat reduction with increasing SJL (p < 0.05).Fig. 1 Body weight / fat change and Social Jet Lag. *p < 0.05, ***p < 0.001; ANOVA; post-hoc analysis (TUKEY-HSD)

SJL and Diet

Table 3 shows the results of a multivariable regression analysis examining the effect of SJL on the BMI decrease rate, adjusting for gender, age, engagement duration, initial BMI, MSFsc, protein intake, carbohydrate intake, and fat intake. Model 1 adjusted for gender, age, engagement duration, and initial BMI. SJL had a significant effect on the change in BMI after adjustment (regression coefficient = 0.589%/hour, 95% Confidence interval (95% CI) = 0.212 to 0.966, p = 0.002). This model explained 17.4% of the change in BMI (adjusted R2 = 0.174). In Model 2, MSFsc was added as a covariate to the regression, and the results remained similar. SJL continued to affect the change in BMI (0.586%/hour, 95% CI = 0.204 to 0.968, p = 0.003, adjusted R2 = 0.174). In Model 3, protein intakes, carbohydrate intakes, and fat intakes were also adjusted additionally, and again, SJL continued to have an effect on the change in BMI (0.561%/hour, 95% CI = 0.189–0.933, p = 0.003, adjusted R2 = 0.196).Table 3 Factors associated with body weight change

	Independent variables	Coefficients (%)	95% Confidence interval (%)	Standardized coefficients	p	VIF	Adjusted R2	
Model 1	Gender (female vs. male)	 − 0.845	 − 1.315: − 0.374	 − 0.056	 < 0.001	1.083	0.174	
	Age (/year)	 − 0.064	 − 0.088: − 0.041	 − 0.085	 < 0.001	1.077	
	Engagement duration (/day)	 − 0.002	 − 0.003: − 0.001	 − 0.062	 < 0.001	1.053	
	Initial BMI (/kg/m2)	 − 0.601	 − 0.647: − 0.555	 − 0.403	 < 0.001	1.051	
	Social jet Lag (/hour)	0.589	0.212: 0.966	0.048	0.002	1.017	
Model 2	Gender (female vs. male)	 − 0.845	 − 1.316: − 0.374	 − 0.056	 < 0.001	1.083	0.174	
	Age (/year)	 − 0.064	 − 0.088: − 0.040	 − 0.085	 < 0.001	1.133	
	Engagement duration (/day)	 − 0.002	 − 0.003: − 0.001	 − 0.062	 < 0.001	1.053	
	Initial BMI (/kg/m2)	 − 0.601	 − 0.647: − 0.555	 − 0.403	 < 0.001	1.051	
	Social jet Lag (/hour)	0.586	0.204: 0.968	0.047	0.003	1.042	
	MSFsc (/hour)	0.009	 − 0.181: 0.199	0.001	0.926	1.091	
Model 3	Gender (female vs. male)	 − 0.262	 − 0.804: 0.279	 − 0.018	0.342	1.471	0.196	
	Age (/year)	 − 0.066	 − 0.090: − 0.042	 − 0.087	 < 0.001	1.104	
	Engagement duration (/day)	 − 0.002	 − 0.003: − 0.001	 − 0.073	 < 0.001	1.099	
	Initial BMI (/kg/m2)	 − 0.593	 − 0.640: − 0.546	 − 0.398	 < 0.001	1.131	
	Social jet Lag (/hour)	0.561	0.189: 0.933	0.045	0.003	1.017	
	Carbohydrate intake (g/day)	0.021	0.013: 0.029	0.085	 < 0.001	1.099	
	Fat intake (g/day)	0.064	0.049: 0.080	0.138	 < 0.001	1.180	
	Protein intake (g/day)	 − 0.012	 − 0.024: 0.000	 − 0.034	0.052	1.336	
Model 2 Chronotype adjusted; Model 3 Food consumption adjusted; BMI Body Mass Index; MSFsc Midoint of Sleep on free-days sleep-corrected

Table 4 shows the results of multivariable regression analysis to examine the effect of SJL on the decrease rate in body fat with adjusting gender, age, engagement duration, initial BMI, MSFsc, protein intakes, carbohydrate intakes, and fat intakes. Model 1 adjusted for gender, age, engagement duration, and initial %body fat. SJL had a significant effect on the change in %body fat after adjustment (regression coefficient = 1.371%/hour, 95% CI = 0.847 to 1.894, p < 0.001, adjusted R2 = 0.098) In Model 2, MSFsc was added as a covariate to the regression, and the results remained similar. SJL continued to affect the change in %body fat (1.214%/hour, 95% CI = 0.683 to 1.746, p < 0.001, adjusted R2 = 0.098). In Model 3, protein intakes, carbohydrate intakes, and fat intakes were also adjusted additionally, and again, SJL still had a significant effect on the change in %body fat (1.402%/hour, 95% CI = 0.456–2.348, p = 0.004, adjusted R2 = 0.114).Table 4 Factors associated with body fat change

	Independent variables	Coefficients (%)	95% Confidence interval (%)	Standardized coefficients	p	VIF	Adjusted R2	
Model 1	Gender (female vs. male)	8.095	7.377: 8.813	0.229	 < 0.001	1.405	0.098	
	Age (/year)	 − 0.169	 − 0.197: − 0.142	 − 0.106	 < 0.001	1.050	
	Engagement duration (/day)	 − 0.014	 − 0.016: − 0.013	 − 0.148	 < 0.001	1.031	
	Initial %body fat	 − 0.480	 − 0.521: − 0.438	 − 0.236	 < 0.001	1.398	
	Social jet Lag (/hour)	1.371	0.847: 1.894	0.045	 < 0.001	1.021	
Model 2	Gender (female vs. male)	8.091	7.373: 8.808	0.229	 < 0.001	1.405	0.098	
	Age (/year)	 − 0.158	 − 0.187: − 0.129	 − 0.099	 < 0.001	1.112	
	Engagement duration (/day)	 − 0.014	 − 0.016: − 0.013	 − 0.148	 < 0.001	1.031	
	Initial %body fat	 − 0.482	 − 0.523: − 0.440	 − 0.236	 < 0.001	1.399	
	Social jet Lag (/hour)	1.214	0.683: 1.746	0.040	 < 0.001	1.053	
	MSFsc (/hour)	0.412	0.166: 0.658	0.030	0.001	1.107	
Model 3	Gender (female vs. male)	10.363	8.734: 11.993	0.286	 < 0.001	2.066	0.114	
	Age (/year)	 − 0.163	 − 0.224: − 0.103	 − 0.089	 < 0.001	1.105	
	Engagement duration (/day)	 − 0.006	 − 0.008: − 0.003	 − 0.078	 < 0.001	1.096	
	Initial %body fat	 − 0.592	 − 0.676: − 0.508	 − 0.274	 < 0.001	1.529	
	Social jet Lag (/hour)	1.402	0.456: 2.348	0.047	0.004	1.017	
	Carbohydrate intake (g/day)	0.044	0.025: 0.064	0.075	 < 0.001	1.105	
	Fat intake (g/day)	0.183	0.145: 0.221	0.162	 < 0.001	1.174	
	Protein intake (g/day)	 − 0.043	 − 0.074: − 0.012	 − 0.050	0.006	1.318	
Model 2 Chronotype adjusted; Model 3 Food consumption adjusted; MSFsc Midoint of Sleep on free-days sleep-corrected

Sensitivity analysis

As sensitivity analysis, average sleep duration and chronotype (MSFsc) were adjusted (Supplemental Table 1 and Supplemental Table 2). With regard to sleep duration, it was not significant in body weight change and had tiny effects on body fat change. However, the effect disappeared after adjusting for food consumption, while SJL remained a significant factor. When adjusting for chronotype, both chronotype and SJL had a significant effect on body weight and body fat change, and SJL had a greater effect than chronotype.

Discussion

In this study, we demonstrated that SJL was associated with a decreased effectiveness of exercise and nutritional instruction on weight and body fat loss. The effect of SJL on the decrease in body weight and fat was found to be significant even after considering the effects of gender, age, duration of observation, initial body weight and fat, chronotype, and dietary intake. SJL can be improved through increased awareness, and that it could be crucial in the pursuit of efficient dieting. Numerous previous studies have demonstrated an association between SJL and obesity; however, most of these studies were cross-sectional. Therefore, a longitudinal evaluation was necessary to clarify the association between SJL and obesity [27]. The present study, which analyzed outcomes after a certain period, offers a more definitive insight into the association than cross-sectional designs. Improving dietary habits and physical activity is crucial in addressing obesity. In this study, it was observed that a lower SJL is associated with the effects of these interventions, suggesting a potential public health implication.

Previous studies reported that individuals with a night-type chronotype are at a higher risk of obesity [28]. However, the mechanisms linking chronotype with obesity have not been clarified to date. In the present study, a multivariable analysis was performed with SJL and chronotype as explanatory variables, and MSFsc was not found to be a significant variable. Individuals with an eveningness chronotype are likely to have a greater SJL, which could act as a confounding factor. It is possible that the increased SJL associated with eveningness contributes to weight loss resistance, rather than the mere presence of an eveningness chronotype. On the other hand, being a night-type or having short sleep duration is reported to contribute to weight gain through increased food intake [29]. In this study, the diet program provided detailed guidance on food intake, and the lack of a significant link between chronotype and food intake might have reduced its impact on changes in body weight and body fat. Additionally, sleep duration, which was also not detected as a significant factor in the sensitivity analysis, despite many studies indicating that short sleep duration tends to lead to obesity [30, 31], may not have shown significance for similar reasons.

Compared with recent studies, the mean value of SJL in this study population is low. In China, it has been reported that 17% of participants had an SJL exceeding 1 h [23], and in Germany, within a work environment that includes shift work, an average SJL of 1.96 h [32] has been documented. In a recent survey of the general Japanese population, the average SJL was 0.91 h, with 40% of participants exceeding 1 h [24]. However, in this study, the average SJL was 24 min (0.6 h), and only 10.3% exceeded 1 h. The reason for this difference is unknown. These studies indicate that younger individuals tend to have larger SJLs, our study included a few younger participants, which might have influenced the lower observed value. Additionally, there could be a sampling bias, as individuals able to attend a personal training gym might not be engaged in occupations that require much overtime work, potentially excluding those with extreme SJL values. This study has some more limitations. First, the data used in this study was self-reported, not using actigraphy or any other objective methods, which may compromise its accuracy. SJL and chronotypes were evaluated by using self-administrated clock times, and the objective internal circadian rhythm indicators, such as the time of dim light melatonin onset, rectal temperature, or clock gene expressions, were not directly measured. Moreover, when using MTCQ methods to calculate SJL and MSFsc, the data from potential shift-workers were not excluded since the occupation and work styles of participants were not assessed in this study. Although the distribution of SJL was in a relatively lower and narrow range, the inaccuracies of these indicators still remain. Seconds, the adequacy of dietary restrictions and the heterogeneity of training loads among participants is unknown. The mean self-reported average daily total caloric intakes were low, suggesting high compliance with this weight loss program. However, the fidelity of the intervention was not directly assessed in this study and was not adjusted in the analyses. Furthermore, the baseline dietary intakes and physical activity levels before the intervention were also unknown. These data were not adjusted in this study. Third, there may be other confounders. The absolute value of SJL is likely to be larger among shift workers or participants with long working hours [33]. Those with limited free time may not have undergone sufficient physical activity in their daily life outside of the program’s training. Furthermore, not only SJL based on sleep–wake schedules, but also whole life schedule including mealtime schedules could affect dietary outcomes. Variable eating patterns cause adiposity and worsen glycemic control [34], which might contribute to obesity and adverse dietary outcomes. There may also be other lifestyle factors that increase SJL and contribute to the worsening of obesity, but not all potential confounding factors were investigated in this study. Fourth, this this study consists of a large sample size with over 10,000 participants, and not restricted to any specific clinical populations other than obesity, suggesting that its external validity may be relatively robust. However, the study was conducted among Japanese individuals, introducing an inherent racial bias. The extent to which these findings can be directly applied to other racial or ethnic groups remains uncertain, highlighting the need for future research in diverse populations.

Several limitations underlie in this study. First, a significant challenge is the unclear mechanisms of these results. Existing studies have suggested a correlation between SJL and increased insulin resistance and insulin secretion [15], as well as elevated cortisol levels [16]. However, this study did not investigate the physiological changes associated with increased SJL. Second, it is unclear whether these results can be generalized to all populations. This is because the study was conducted only in Japan, mainly composed of East Asian people, and did not examine the influence of ethnicity on the negative effects of SJL on obesity and metabolism. Third, although food consumption was adjusted for in the analyses, other factors that may affect diet, obesity, and health, such as alcohol intake, tobacco use, or any other supplement intake, were not examined. Especially in the case of alcohol intake, weekend drinking could be associated not only with obesity and dieting but also with the occurrence of social jet lag (SJL). Measuring and adjusting for these factors in future studies will be needed. Forth, this study is an observational survey and does not involve a randomized comparative trial. Therefore, it does not provide complete proof of a causal association between SJL and obesity. Specifically, a randomized controlled trial comparing a group in which SJL is reduced through guidance with a control group, while controlling for various conditions, is needed to determine whether improving SJL promotes improvement in obesity.

Conclusions

Social jetlag, the misalignment between inherent circadian sleep–wake rhythms and socially required sleep schedules, was associated with the effect of exercise and nutrition instruction on BMI and body fat reduction, even after adjustment for covariates. Maintaining appropriate sleep–wake rhythms may play a role in the pursuit of efficient dieting.

Supplementary Information

Below is the link to the electronic supplementary material.Supplementary file1 (XLSX 17 KB)

Author contribution

KM: Writing—Original Draft, Formal analysis, Visualization, AS: Conceptualization, Methodology, Formal analysis, Investigation, Writing—Original Draft, Writing—Review & Editing, Project administration KS: Methodology, Software, Investigation, Data Curation, HH: Writing—Review & Editing, Visualization, YA: Software, Resources, Data Curation, TS: Investigation, Resources, Data Curation, MY: Investigation, Resources, Data Curation, JM: Writing—Review & Editing, Supervision, YT: Writing—Review & Editing, Supervision, TI: Writing—Review & Editing, Supervision, Project administration.

Funding

Open access funding provided by Tokyo Medical University. No external funding was received for this research.

Declarations

Conflicts of interest

The anonymized dataset of this study was provided by RIZAP Inc. (Shinjuku, Tokyo, Japan) free of charge. AS and Children & Future Co., Ltd. have an issued patent (P6722911), and a pending patent (PCT/JP2020/016435). TS is a board member of RIZAP Inc. The other authors declare there are no conflicts of interest directly relevant to this study.

Ethical committee permission

This study was conducted with approval from the Tokyo Medical University Medical Ethics Review Committee (study approval no.: T2018-0041).

Research involving

Human Participants.

Informed consent

Upon joining the program and before starting their exercise and nutrition regimen, participants provided informed consent that their data might be used for future academic analyses in an anonymous manner.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References

1. Collaboration NRF Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19·2 million participants Lancet (London, England) 2016 387 10026 1377 10.1016/S0140-6736(16)30054-X 27115820
Collaboration NRF. Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19·2 million participants. Lancet (London, England). 2016;387(10026):1377.27115820
2. Al-Goblan AS Al-Alfi MA Khan MZ Mechanism linking diabetes mellitus and obesity Diabetes Metab Syndr Obes. 2014 7 587 591 10.2147/DMSO.S67400 25506234
Al-Goblan AS, Al-Alfi MA, Khan MZ. Mechanism linking diabetes mellitus and obesity. Diabetes Metab Syndr Obes. 2014;7:587–91. 10.2147/DMSO.S67400.25506234
3. Hall ME Obesity, hypertension, and chronic kidney disease Int J Nephrol Renov Dis 2014 18 7 75 88 10.2147/IJNRD.S39739
Hall ME, et al. Obesity, hypertension, and chronic kidney disease. Int J Nephrol Renov Dis. 2014;18(7):75–88.
4. Ortega FB Lavie CJ Blair SN Obesity and cardiovascular disease Circ Res 2016 118 11 1752 1770 10.1161/CIRCRESAHA.115.306883 27230640
Ortega FB, Lavie CJ, Blair SN. Obesity and cardiovascular disease. Circ Res. 2016;118(11):1752–70.27230640
5. Choi HK Obesity, weight change, hypertension, diuretic use, and risk of gout in men: the health professionals follow-up study Arch Intern Med 2005 165 7 742 748 10.1001/archinte.165.7.742 15824292
Choi HK, et al. Obesity, weight change, hypertension, diuretic use, and risk of gout in men: the health professionals follow-up study. Arch Intern Med. 2005;165(7):742–8.15824292
6. Jung UJ Choi M-S Obesity and its metabolic complications: the role of adipokines and the relationship between obesity, inflammation, insulin resistance, dyslipidemia and nonalcoholic fatty liver disease Int J Mol Sci 2014 15 4 6184 6223 10.3390/ijms15046184 24733068
Jung UJ, Choi M-S. Obesity and its metabolic complications: the role of adipokines and the relationship between obesity, inflammation, insulin resistance, dyslipidemia and nonalcoholic fatty liver disease. Int J Mol Sci. 2014;15(4):6184–223.24733068
7. Gami AS Caples SM Somers VK Obesity and obstructive sleep apnea Endocrinol Metab Clin 2003 32 4 869 894 10.1016/S0889-8529(03)00069-0
Gami AS, Caples SM, Somers VK. Obesity and obstructive sleep apnea. Endocrinol Metab Clin. 2003;32(4):869–94.
8. Lementowski PW Zelicof SB Obesity and osteoarthritis American Journal of Orthopedics-Belle Mead- 2008 37 3 148
Lementowski PW, Zelicof SB. Obesity and osteoarthritis. American Journal of Orthopedics-Belle Mead-. 2008;37(3):148.
9. Silvestris E Obesity as disruptor of the female fertility Reprod Biol Endocrinol 2018 16 1 13 10.1186/s12958-018-0336-z 29310676
Silvestris E, et al. Obesity as disruptor of the female fertility. Reprod Biol Endocrinol. 2018;16:1–13.29310676
10. Galtier-Dereure F Boegner C Bringer J Obesity and pregnancy: complications and cost Am J Clin Nutr 2000 71 5 1242S 1248S 10.1093/ajcn/71.5.1242s 10799397
Galtier-Dereure F, Boegner C, Bringer J. Obesity and pregnancy: complications and cost. Am J Clin Nutr. 2000;71(5):1242S-1248S.10799397
11. Flegal KM Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis JAMA 2013 309 1 71 82 10.1001/jama.2012.113905 23280227
Flegal KM, et al. Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis. JAMA. 2013;309(1):71–82.23280227
12. Wittmann M Social jetlag: misalignment of biological and social time Chronobiol Int 2006 23 1–2 497 509 10.1080/07420520500545979 16687322
Wittmann M, et al. Social jetlag: misalignment of biological and social time. Chronobiol Int. 2006;23(1–2):497–509.16687322
13. Parsons MJ Social jetlag, obesity and metabolic disorder: investigation in a cohort study Int J Obes 2015 39 5 842 848 10.1038/ijo.2014.201
Parsons MJ, et al. Social jetlag, obesity and metabolic disorder: investigation in a cohort study. Int J Obes. 2015;39(5):842–8.
14. Roenneberg T Social jetlag and obesity Curr Biol 2012 22 10 939 943 10.1016/j.cub.2012.03.038 22578422
Roenneberg T, et al. Social jetlag and obesity. Curr Biol. 2012;22(10):939–43.22578422
15. Wong PM Social jetlag, chronotype, and cardiometabolic risk J Clin Endocrinol Metab 2015 100 12 4612 4620 10.1210/jc.2015-2923 26580236
Wong PM, et al. Social jetlag, chronotype, and cardiometabolic risk. J Clin Endocrinol Metab. 2015;100(12):4612–20.26580236
16. Rutters F Is social jetlag associated with an adverse endocrine, behavioral, and cardiovascular risk profile? J Biol Rhythms 2014 29 5 377 383 10.1177/0748730414550199 25252710
Rutters F, et al. Is social jetlag associated with an adverse endocrine, behavioral, and cardiovascular risk profile? J Biol Rhythms. 2014;29(5):377–83.25252710
17. Koopman AD The association between social jetlag, the metabolic syndrome, and type 2 diabetes mellitus in the general population: the new Hoorn study J Biol Rhythms 2017 32 4 359 368 10.1177/0748730417713572 28631524
Koopman AD, et al. The association between social jetlag, the metabolic syndrome, and type 2 diabetes mellitus in the general population: the new Hoorn study. J Biol Rhythms. 2017;32(4):359–68.28631524
18. Sűdy ÁR Association of social jetlag with sleep quality and autonomic cardiac control during sleep in young healthy men Front Neurosci 2019 13 472752 10.3389/fnins.2019.00950
Sűdy ÁR, et al. Association of social jetlag with sleep quality and autonomic cardiac control during sleep in young healthy men. Front Neurosci. 2019;13: 472752.
19. Levandovski R Depression scores associate with chronotype and social jetlag in a rural population Chronobiol Int 2011 28 9 771 778 10.3109/07420528.2011.602445 21895489
Levandovski R, et al. Depression scores associate with chronotype and social jetlag in a rural population. Chronobiol Int. 2011;28(9):771–8.21895489
20. Takaesu Y Association of sleep duration on workdays or free days and social jetlag with job stress Psychiatry Clin Neurosci 2021 75 8 244 249 10.1111/pcn.13274 34008308
Takaesu Y, et al. Association of sleep duration on workdays or free days and social jetlag with job stress. Psychiatry Clin Neurosci. 2021;75(8):244–9.34008308
21. Fárková E Weight loss in conservative treatment of obesity in women is associated with physical activity and circadian phenotype: A longitudinal observational study BioPsychoSocial Medicine 2019 13 1 10 10.1186/s13030-019-0163-2 30733824
Fárková E, et al. Weight loss in conservative treatment of obesity in women is associated with physical activity and circadian phenotype: A longitudinal observational study. BioPsychoSocial Medicine. 2019;13:1–10.30733824
22. Beauvalet JC Social jetlag in health and behavioral research: a systematic review ChronoPhysiology and Therapy 2017 7 19 31 10.2147/CPT.S108750
Beauvalet JC, et al. Social jetlag in health and behavioral research: a systematic review. ChronoPhysiology and Therapy. 2017;7:19–31.
23. Zhang Z Cajochen C Khatami R Social jetlag and chronotypes in the Chinese population: analysis of data recorded by wearable devices J Med Internet Res 2019 21 6 e13482 10.2196/13482 31199292
Zhang Z, Cajochen C, Khatami R. Social jetlag and chronotypes in the Chinese population: analysis of data recorded by wearable devices. J Med Internet Res. 2019;21(6): e13482.31199292
24. Komada Y A survey on social jetlag in Japan: A nationwide, cross-sectional internet survey Sleep Biol Rhythms 2019 17 417 422 10.1007/s41105-019-00229-w
Komada Y, et al. A survey on social jetlag in Japan: A nationwide, cross-sectional internet survey. Sleep Biol Rhythms. 2019;17:417–22.
25. Roenneberg T Wirz-Justice A Merrow M Life between clocks: daily temporal patterns of human chronotypes J Biol Rhythms 2003 18 1 80 90 10.1177/0748730402239679 12568247
Roenneberg T, Wirz-Justice A, Merrow M. Life between clocks: daily temporal patterns of human chronotypes. J Biol Rhythms. 2003;18(1):80–90.12568247
26. Roenneberg T A marker for the end of adolescence Curr Biol 2004 14 24 R1038 R1039 10.1016/j.cub.2004.11.039 15620633
Roenneberg T, et al. A marker for the end of adolescence. Curr Biol. 2004;14(24):R1038–9.15620633
27. Rusu A Variability in sleep timing and dietary intake: a scoping review of the literature Nutrients 2022 14 24 5248 10.3390/nu14245248 36558406
Rusu A, et al. Variability in sleep timing and dietary intake: a scoping review of the literature. Nutrients. 2022;14(24):5248.36558406
28. Muscogiuri G The EASO New Investigator Award in Clinical Research 2021: role of chronotype in obesity Obes Facts 2023 16 2 131 140 10.1159/000527691 36349806
Muscogiuri G. The EASO New Investigator Award in Clinical Research 2021: role of chronotype in obesity. Obes Facts. 2023;16(2):131–40.36349806
29. Chaput J-P Sleep patterns, diet quality and energy balance Physiol Behav 2014 134 86 91 10.1016/j.physbeh.2013.09.006 24051052
Chaput J-P. Sleep patterns, diet quality and energy balance. Physiol Behav. 2014;134:86–91.24051052
30. Bayon V Sleep debt and obesity Ann Med 2014 46 5 264 272 10.3109/07853890.2014.931103 25012962
Bayon V, et al. Sleep debt and obesity. Ann Med. 2014;46(5):264–72.25012962
31. Guimarães KC Is self-reported short sleep duration associated with obesity? a systematic review and meta-analysis of cohort studies Nutr Rev 2022 80 5 983 1000 10.1093/nutrit/nuab064 34508648
Guimarães KC, et al. Is self-reported short sleep duration associated with obesity? a systematic review and meta-analysis of cohort studies. Nutr Rev. 2022;80(5):983–1000.34508648
32. Yong M Are chronotype, social jetlag and sleep duration associated with health measured by Work Ability Index? Chronobiol Int 2016 33 6 721 729 10.3109/07420528.2016.1167728 27092579
Yong M, et al. Are chronotype, social jetlag and sleep duration associated with health measured by Work Ability Index? Chronobiol Int. 2016;33(6):721–9.27092579
33. Casjens S Social jetlag and sleep debts are altered in different rosters of night shift work PLoS ONE 2022 17 1 e0262049 10.1371/journal.pone.0262049 34995309
Casjens S, et al. Social jetlag and sleep debts are altered in different rosters of night shift work. PLoS ONE. 2022;17(1): e0262049.34995309
34. Makarem N Variability in daily eating patterns and eating jetlag are associated with worsened cardiometabolic risk profiles in the american heart association go red for women strategically focused research network J Am Heart Assoc 2021 10 18 e022024 10.1161/JAHA.121.022024 34482703
Makarem N, 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. J Am Heart Assoc. 2021;10(18): e022024.34482703
