Social jetlag predicts greater evening energy intake in a UK cohort

Social jetlag predicts greater evening energy intake in a UK cohort | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Social jetlag predicts greater evening energy intake in a UK cohort Alan Flanagan, Barbara A Fielding, Jonathan D Johnston, Eva Winnebeck This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6662028/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Social jetlag (SJL) describes the differences in sleep timing between workdays and weekends. In a 14-day longitudinal observational study, we explored the associations between SJL and energy intake on workdays and weekends. Healthy male and female participants residing in the UK [ n = 101] used a smartphone app to record dietary intake continuously during the study period, encompassing two working weeks and weekends. We hypothesised that higher SJL levels would be associated with greater energy intake after 16:00 h and that any such effect would be most evident in late chronotypes. We found that increasing SJL levels modified temporal energy intake in both female and male participants, while temporal patterns of energy intake differed by sex. The modifying effect of SJL differed slightly between weekdays and weekends. Inclusion of chronotype in the statistical models did not alter the associations between SJL and energy intake. Furthermore, the magnitude of the effect estimates was modest and, in caloric terms, unlikely to explain the higher risks of obesity and metabolic diseases previously associated with increasing SJL levels. Biological sciences/Cell biology/Circadian rhythms Health sciences/Diseases Diet nutrition chrono-nutrition circadian meal timing Figures Figure 1 Figure 2 Introduction ‘Circadian rhythms’ are biological rhythms that are endogenously generated with a period length of approximately 24 h, and entrain to external environmental time cues (1,2). These rhythms modulate most aspects of physiology, including metabolism and responses to food (2–4) Circadian entrainment is achieved when external time cues are aligned with internal clock timing and continually reinforced through the timing of behaviours and environmental signals (1). Circadian misalignment occurs where the timing of environmental time-cues [e.g., the light-dark cycle] and/or behavioural cycles [e.g., sleep/wake and/or feeding/fasting] are misaligned with the phase angle of endogenous SCN rhythms (5). One emerging concept that represents a form of circadian misalignment is ‘social jetlag’ (SJL). SJL describes the differences between sleep timing on work days, with enforced wake times, and free days or weekends, creating a discordance between internal biological time and social timing that can lead to a chronic form of jetlag (6). ‘Chronotype’ denotes an individual’s behavioural expression of sleep-wake timing preferences, expressed as a continuous variable ranging from early ‘morning larks’ to late ‘night owls’, related to the phase of their internal biological time (7). To date, an evening chronotype has been associated with adverse health behaviours and outcomes (8,9). It has previously been shown that chronotype-specific sleep onset exists on workdays, despite similar social clock wake times, indicating that the social clock is not strong enough to advance the circadian clock or phase of sleep (7). Therefore, evening chronotypes accumulate more sleep debt during the week, while morning types suffer the opposite on weekends/free days, due to social pressure to stay up later (7). Several authors have posited that the relationship between chronotype, social behaviours, and timing of food intake may be mediated by SJL (6,7,10,11). SJL of > 2 h has been associated with higher cardiometabolic risk factors, including inflammatory markers, fasting glucose, waist circumference, and higher odds for obesity and metabolic syndrome (10,12). SJL may exert negative influences on self-regulation and health-promoting behaviours, influencing dietary intake and health status (13). Whether the adverse associations between SJL and metabolic health may reflect biological factors, i.e., rhythms in glucose metabolism (14,15) or social factors, i.e., sleep debt and delayed meal timing (16,17), is unclear. However, it indicates that behavioural influences on meal timing may have an inherent time-of-day preference component. To date, the majority of research in relation to SJL comprises cross-sectional studies (10,12,16,17). The primary aim of the present study was to longitudinally investigate the relationship between SJL and energy intake, between weekdays and weekends. Secondary aims included investigating the associations between SJL and meal timing between weekdays and weekends. We hypothesised that higher SJL levels would be associated with greater energy intake later in the day. Methods Study Design and Population A longitudinal, observational study was conducted over 14 days, designed to capture two full working weeks and two full weekends. A favourable ethical opinion for the study was obtained from the University of Surrey Ethics Committee [FHMS 20–21 096 EGA], and written informed consent was obtained from all participants before entry into the study. Eligibility criteria comprised of healthy adult male and female participants aged between 25–50 y in full-time occupations, no current night-shift work, no sleep disorders or sleep-enhancing nutrition/herbal supplements (e.g., melatonin), and < 14 units alcohol per week. Recruitment was conducted through online adverts on social media and email. 110 participants completed screening, of which eight withdrew before or during the beginning of the diet data collection period; one further participant was excluded due to incomplete diet diaries. The final sample analysed here included 101 participants. The study ran in four separate cohorts between April to September 2021. Dietary Assessment Dietary intake was tracked using a photographic mobile phone application, ‘ See How You Eat Food Diary App ’ (Health Revolution Ltd., Kotka, Finland). This app allowed participants to photographically record all food and beverages consumed, time-stamped with the time of day the photograph was taken and allowed for further detailed written descriptions of the meal contents to be provided. Participants exported each day’s diet diary, inclusive of photographs and meal descriptions, to be emailed to the research team. To be included in the final analysis, compliance was deemed to be a minimum of 2 diet entries per day, on 12 of the 14 days. Each study cohort began on a Monday and ran through 14 days inclusive of two weekends. 91% of the final analytical sample completed a full 14 days of dietary recording. Dietary intake data were analysed using Nutritics (v5.029, Dublin, Ireland), with the 2015 ‘Composition of Foods Integrated Dataset ‘CoFID’, including McCance and Widdowson’s 7th Edition, utilised as the relevant database for diet diary entries. To account for diary entries failing to specify a portion size [i.e. in cups, grams, or spoon measures, or readily identifiable pre-packaged portions], the demographic average portion sizes, based on the UK National Diet and Nutrition Survey for age and sex-specific reported portion sizes was entered for a given food item or beverage ( 18 , 19 ). To be classified as an eating occasion, a minimum energy criterion of 50kcal was applied ( 20 ). The validity of self-reported dietary intake was assessed by testing the agreement between energy intake reported through the app and estimated energy requirements (EER) as the reference measure. Reported energy intake (REI) was calculated as the mean total daily energy for all reported days of dietary intake. EER was estimated for each participant using the Henry-Oxford 2005 equation, factoring in age, height, weight, and reported physical activity level (PAL), where estimated resting metabolic rate (RMR) was multiplied by the PAL ( 21 ). A Bland-Altman comparison was used to test the agreement between average self-reported total daily energy intake over the 14 days of dietary recording and EER. The mean of the differences was − 277 kcal (95% CI of the Mean − 368 to − 186 kcal), and 95% Lower to Upper Limits of Agreement of − 1179 to 624 kcal ( Supplementary Figures S1 & S2; Suppl. Table S1 ). The narrow confidence intervals around the mean difference, i.e., the systematic error, and the limits of agreement largely within ± 1,200 kcal, suggest lower individual-level error, indicating that the data is suitable for group-level analysis ( 22 ). Assessment of Chronotype and Social Jetlag Chronotype was determined using the validated Munich Chronotype Questionnaire (MCTQ), as previously described ( 7 , 23 ). Chronotype was calculated as the midpoint of sleep on free days (i.e., weekends; MSF), where SOf is sleep onset on free days and SDf is sleep duration on free days ( 18 ): $$\:MSF\:=\:SOf\:+\:\frac{SDf}{2}$$ Which was corrected for oversleeping in participants who slept longer on weekends compared to weekdays using the equation from Roenneberg et al. ( 23 ): $$\:MSFsc=MSF\:-\:\frac{\left(SDf-SDweek\right)}{2}\:=SOf\:+\:\frac{SDweek}{2}$$ Where MSF is the midpoint of sleep on free days, MSFsc is the midpoint of sleep corrected for sleep debt on free days, SDweek is the average weekly sleep duration, SOf is sleep onset on free days, and SDf is sleep duration on free days. MSFsc has been shown to correlate strongly [ r = .68] with the dim-light melatonin onset [DLMO], an objective laboratory measure of internal circadian phase ( 24 ). Social jetlag [SJL] was also determined using the MCTQ ( 6 ), and calculated as the difference between the midpoint of sleep on workdays and weekends ( 6 ): $$\:SJL=MSF-MSW\:$$ Where MSF is the midpoint of sleep on free days and MSW is the midpoint of sleep on workdays. Distributions of SJL and chronotype in the study cohort are presented in Suppl. Figures S3 & S4 . Covariates Covariates including age, sex, height, weight, calculated body mass index (BMI; weight [kg] / height squared [meters 2 ]), work location, dependent children in the home, smoking status, and weekly alcohol intake, were collected using a self-reported baseline screening questionnaire and included in the statistical models. Statistical Analysis Categorical variables were summarised as number and percentages, while continuous variables were summarised as mean and standard deviation. Energy intake was separated in 4-h time-bins: 04:00–08:00 h (‘Early Morning’); 08:00–12:00 h (‘Late Morning’); 12:00–16:00 h (‘Afternoon’); 16:00–20:00 h (‘Early Evening’), and 20:00–00:00 h (‘Late Evening’), and for two conditions, ‘Weekday’ and ‘Weekend’, for each time-bin. The primary exposure of interest was SJL. The primary outcomes of interest were overall energy intake on workdays and weekends. Data were analysed using random-effects linear mixed models to assess differences in temporal energy intake longitudinally over 14 days. Models were fitted using restricted maximum likelihood to all observed energy intake data for each of the 101 participants included in the final analysis. Each model consisted of a random intercept for individual participants to account for within-participant correlations in the observed longitudinal data. The models contained fixed effects for SJL and chronotype, in addition to the continuous variables of mean-centred age, BMI, and alcohol intake, and categorical variables including sex (reference level: female), work location (reference level: external job site), dependent children (yes/no), and smoking status (reference level: never), as covariates. Covariates were determined on a theoretical basis according to the wider literature related to factors which may influence sleep-wake cycles, chronotype, and related dietary behaviours ( 1 , 6 , 7 , 25 ). The models allowed for testing differences in energy intake according to time-bin, SJL, and sex of participants, including their two- and three-way interactions. Models were further analysed and visualised using estimated marginal means and post-hoc pairwise comparisons with Tukey correction for multiple comparisons. We generated three separate models in our data analysis. Across these, SJL was modelled both as a categorical and continuous variable. In Model 1, SJL was categorised as 1 h, approximate to the mean SJL level of 58 min or 0.97 h in the participants and consistent with previous research examining diet and SJL ( 16 , 17 ). In Model 2, SJL was treated as a continuous variable to investigate the influence of categorisation on the results. In Model 3, sensitivity analyses were conducted by trichotomising SJL in distribution-based interquartile ranges of top 25% [> 75 mins], moderate 50% [27–75 mins], and low 25% [< 27 mins] SJL. We also conducted sensitivity analyses on Model 1 by excluding chronotype from the models to determine whether chronotype influenced any associations observed for SJL. All data were analysed using the lmer and emmeans packages in R v4.3.2 (R Core Team, The R Foundation for Statistical Computing, 2023), and graphing was conducted using GraphPad Prism v9.3.1 (GraphPad Software 2021, La Jolla California, USA). Outcomes are presented as Beta (unstandardised) coefficients with 95% confidence intervals (CI) and conditional R 2 , the latter of which considers both fixed and random effects in the model. Results Demographics and descriptive statistics for the cohort can be found in Table 1 . The study sample comprised 77.5% females and 22.5% males with a mean age of 32.7 (± 6.5yrs), and mean BMI of 23.7 kg/m 2 (± 3.7). Mean SJL was 58 min (± 41 min; range 0-203 min), while mean chronotype (as MSF SC ) was 03:25 h (± 00:54 h; range 01:26 – 06:10 h). Associations Between Energy Intake and 1 h Social Jetlag (Model 1) Figure 1 displays results based on marginal means derived from the linear mixed models, while full model coefficients, 95% CI, and P -values may be found in Table 2. Results of post-hoc comparisons are listed in Table 3 .The linear mixed model for weekdays explained 23.9% and for weekends 26.9% of the variance in energy intake ( Table 2 ). Overall, energy intake increased sequentially from before 08:00 h (the reference category) to the 16:00–20:00 h time bin, before declining in the 20:00–00:00 h time bin on both weekdays and weekends. This temporal pattern was modulated by sex, whereby male participants exhibited a greater peak in energy intake, particularly in the evening (16:00-20:00 h) during both weekdays and weekends ( Figure 1 ). Importantly, SJL also modulated this temporal pattern but with different effects in females and males. In female participants, the overall pattern indicated greater energy intake earlier in the day in the lower SJL group than in the higher SJL group. Although effect sizes and statistical significances in post-hoc pairwise comparisons varied between weekday and weekend models, the pattern was qualitatively similar between both day types ( Fig.1, Tables 2 & 3 ). On weekdays, post-hoc pairwise comparisons indicated statistically significantly lower energy intake in female participants with >1 h SJL than those with <1 h SJL both in the early morning (04:00–08:00 h) and the early evening (16:00-20:00) by 81 kcal (95% CI, –141 to –22 kcal) and 70 kcal (–130 to –11 kcal), respectively ( Fig. 1, Table 3 ). Conversely, energy intake was higher in the late evening (20:00-00:00) by 100 kcal (41 to 160 kcal) for females in the >1h SJL category. On weekends, post-hoc comparisons only indicated a statistically significant difference in the late morning (08:00–12:00 h), where females with > 1 h SJL exhibited 101 kcal (–185 to –16 kcal) lower energy intake compared to females with < 1 h SJL. By contrast, in male participants, who constituted only a small proportion of the overall sample, SJL appeared to be only associated with temporal energy intake in the afternoon, most predominantly on weekends. In males with > 1 h SJL, post-hoc pairwise comparisons indicated that energy intake on weekend afternoons (12:00–16:00 h) was 249 kcal (–405 to –93 kcal) lower compared to males with <1 h SJL (Table 3). Overall, the results indicate that temporal energy intake varied based on both SJL level and participants' sex, which interacted to influence temporal patterns of energy intake in our sample. Associations Between Energy Intake and Social Jetlag as a Continuous Variable (Model 2) Modelling SJL as a continuous variable showed similar results to the categorical models and explained 24.0% of the variance in energy intake on weekdays and 27.0% on weekends ( Tables 4 and 5 ). Marginal means plots can be found in Figure 2 , while full model coefficients, 95% CI, and P -values, may be found in Tables 4 and 5 . Overall, for the larger sample of female participants, the models indicated a clear temporal pattern between SJL and energy intake, matching the findings from the dichotomous model: the higher the SJL, the lower the energy intake in the morning and the higher in the late evening. On weekdays in females, each hour increase in SJL was associated with 51 kcal (–99 to –3 kcal) less energy intake between 08:00–12:00 h and with 66 kcal (18 to 114 kcal) greater energy intake in the late evening between 20:00–00:00 h ( Figure 2 ). In female participants on weekends, a similar temporal pattern was evident: between 08:00–12:00 h, each hour increase in SJL was associated with 72 kcal (–141 to –2 kcal) less energy intake, while between 20:00–00:00 h, with 61 kcal (–8 to 131 kcal) greater energy intake. The pattern in the smaller male sample was more varied than the categorical analysis. Consistent with the dichotomous model (Model 1) was the effect of SJL on energy intake in the early afternoon. In the afternoons between 12:00–16:00 h, each hour increase in SJL was associated with 102 kcal (–174 to –30 kcal) less energy intake on weekdays and 130 kcal (–233 to –26 kcal) less on weekends. However, each hour increase in SJL was associated with 141 kcal (70 to 213 kcal) greater energy intake between 08:00–12:00 h, which was in the opposite direction to females at this time of day. In the early evening period between 16:00–20:00 h, each hour increase in SJL was similarly associated with 148 kcal (76 to 219 kcal) greater energy intake. On weekends, it was the latest time point (20:00–00:00 h) where males showed greater energy intake of 99 kcal (–5 to 202 kcal) with increasing SJL, similar to the pattern observed in female participants at this time of day. Overall, the results of the continuous analysis similarly indicate a modification of energy intake by time, sex and SJL, suggesting that increasing SJL levels differentially associate with energy intake at different times of day in male and female participants, although greater energy intake in the evening with increasing SJL was observed for both sexes. Sensitivity Analyses Sensitivity analyses with SJL trichotomized based on sample distribution as low, moderate and high levels (Model 3), generally corroborated the differences of energy intake with SJL in females observed in the dichotomous model (Model 1). Although overall slightly less congruent owing to lower sample sizes per group, Model 3 yielded additional granularity on the higher energy intake by females in the late evening, indicating that it was driven by participants in the top 25% of SJL levels ( Supp. Table S2; Figure S5 ). In a final sensitivity analysis, chronotype was removed as a fixed effect from Model 1 and the results of the main analyses remained essentially unchanged ( R 2 , 0.239 for weekday energy intake and R 2 , 0.269 for weekend energy intake) ( Supp. Table S3 ). Discussion SJL represents an emerging risk factor that exhibits a high prevalence in the general population, with estimates of up to 46% of the population having > 1 h SJL ( 23 ). Unlike the adaptations to a new time zone that occur with trans-meridian jetlag, SJL represents a more chronic form of circadian misalignment and therefore, as a conceptual exposure, may be more akin to shift work, i.e., a discordance between internal biological timing and external social timing, characterised as a chronic form of jetlag ( 6 , 23 ). The discordance between biological timekeeping and the social clock may exert a strain on the circadian system, which accumulates into adverse metabolic risk associated with greater SJL levels ( 10 , 12 , 17 , 26 ). However, the degree to which these associations may relate to circadian disruption, dietary factors, or an interrelationship of both remains to be fully elucidated. Furthermore, the majority of research on SJL and diet comprises cross-sectional studies ( 10 , 12 , 16 , 17 ). As such, the direction of effect, i.e., whether differences in SJL relate to true differences in patterns of energy intake or diet quality that may precipitate higher metabolic disease risk, remains unclear. In this study, our primary exposure of interest was absolute energy intake, with anticipated sex differences due to sex dimorphism in body size and composition (i.e., lean mass and fat mass). All participants displayed a temporal pattern of increasing energy intake throughout the day, peaking in the early evening [16:00–20:00 h], which aligns with the temporal energy intake patterns previously observed in UK populations ( 27 , 28 ). Our data suggest that SJL may exert a moderating influence on temporal energy intake patterns that differ by sex. However, the magnitude of the effect estimates was modest and, in caloric terms, unlikely to significantly explain the higher risks of obesity and metabolic diseases previously associated with SJL ( 10 , 12 , 26 ). Previous research analysing the association between SJL, categorised dichotomously as in our current study, and dietary intake, has revealed statistically significant, but small magnitude of differences in total and saturated fat intake, dietary cholesterol, servings of beans, and sweets ( 16 , 17 ). Therefore, the extent to which the adverse metabolic consequences of heightened SJL relate to diet or other lifestyle factors remains to be determined. A late chronotype has been associated, to date, with lower diet quality and a redistribution of energy and macronutrient intake to later in the day ( 8 , 9 ). Previous research has suggested that associations between the timing of energy intake and overweight/obesity may be dependent on chronotype (37,38). In addition, several studies have suggested that the relationship between chronotype and diet may be mediated by SJL ( 6 , 10 , 11 ). This may be because higher SJL levels tend to correlate strongly with late chronotypes ( 29 ). On average, intermediate chronotypes with an average midpoint of sleep of 03:00 h exhibit the least SJL, as they have the least differences in sleep timing and duration between work and free days ( 6 , 23 ). In our analysis, removing chronotype from Model resulted in no change in the model estimates, suggesting that chronotype had little influence on the associations between SJL and energy intake. However, in our participants, the mean midpoint of sleep was 03:25 h (± 00:54), in a range where minimal SJL would be expected. To what extent SJL and chronotype interact to influence associations with diet and metabolic health remains to be elucidated and will require larger sample sizes in well-conducted prospective cohort studies to determine. Importantly, the choice of categorisation for SJL as an exposure may influence any observed associations. We categorised SJL based on previous research categorising SJL dichotomously as over or under 1 hour ( 16 , 17 , 26 ) or in tertiles with hourly increments ( 10 , 12 ). However, arbitrary categorisation may result in a loss of power and the potential bias of not accurately capturing variation within the ranges of each category (39,40). Although the overall variance explained by both our categorical and continuous models was similar, modelling SJL as a continuous variable suggested an interaction between increasing SJL levels and sex on temporal energy intake that was not evident when SJL was categorised dichotomously as 1 h weekly SJL. To date, we are unaware of other nutrition research which has modelled SJL as a continuous variable, and given the emerging nature of SJL as a risk factor, future research should consider this approach to investigating associations between SJL, diet, and metabolic risk. Our study has several strengths and limitations. The primary strength of the study is utilising all available data on energy intake in participants from 14 days of longitudinal dietary data collection, rather than mean energy intake for each time point, to fully capture potential variance in the linear models. The study period also captured two full working weeks and two full weekends and thus, the variations observed may be more representative and may have minimised within-person variation that could arise with only one week of data. We adjusted for covariates which could be expected to influence circadian rhythms and/or sleep cycles, including workplace, dependent children, and alcohol intake. Further, the level of compliance with dietary recording was excellent. We used well-validated questionnaires to assess chronotype and social jetlag. However, limitations of the present analysis should be noted.. These include the observational design and potential for unmeasured covariates to influence the observed associations, the small sample size, and self-reported energy intake that indicated the presence of systematic bias and underestimation of energy intake, which is the expected direction of effect with self-reported dietary data (41). In addition, the use of the smartphone application method of dietary assessment was unvalidated against a reference measure of dietary intake. The study sample was predominantly female, and outcomes in the categorical models primarily reflected the influence of energy intake in female participants. Our participants were also primarily recruited through social media platforms and exhibited high levels of a range of health-promoting behaviours. Thus, our sample may not be fully representative of the general population. In conclusion, our data suggest that higher weekly levels of SJL may be associated with greater energy intake in the evening and nighttime. However, the magnitude of the effect estimates was modest and, in caloric terms, unlikely to explain the higher risks of obesity and metabolic diseases previously associated with increasing SJL levels. Future larger prospective and intervention studies could shed additional light on the potential relevance of SJL, irregular temporal eating patterns, and metabolic risk. Declarations Acknowledgments We thank the participants for taking part in the study. This study received no funding. Author Contributions Conceptualisation, A.F., B.A.F., J.D.J.; Methodology, A.F., B.A.F., J.D.J., E.W.; Formal Analysis, A.F., E.W.; Writing – Original Draft, A.F.; Review & Editing, all authors. All authors read and approved the final manuscript. Competing Interests J.D.J. has collaborated with Nestlé and has undertaken consultancy work for Kellogg’s and International Flavors and Fragrances (IFF). A.F. is a director of Alinea Nutrition Ltd. and declares no non-financial competing interests. Data Availability Statement The datasets generated and analysed during the current study are not publicly available but are available from the corresponding author on reasonable request. References Roenneberg T, Daan S, Merrow M. The Art of Entrainment. J Biol Rhythms. 2003;18(3):183–94. Flanagan A, Bechtold DA, Pot GK, Johnston JD. Chrono-nutrition: From molecular and neuronal mechanisms to human epidemiology and timed feeding patterns. J Neurochem. 2021;157(1):53–72. Ruddick-Collins LC, Morgan PJ, Johnstone AM. Mealtime: A circadian disruptor and determinant of energy balance? J Neuroendocrinol. 2020;32(7):1–18. Johnston JD. Physiological responses to food intake throughout the day. Nutr Res Rev. 2014;27(1):107–18. 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Mota MC, Silva CM, Balieiro LCT, Fahmy WM, Crispim CA. Social jetlag and metabolic control in non-communicable chronic diseases: A study addressing different obesity statuses. Sci Rep. 2017;7(1):1–8. Almoosawi S, Vingeliene S, Karagounis LG, Pot GK. Chrono-nutrition: A review of current evidence from observational studies on global trends in time-of-day of energy intake and its association with obesity. Proc Nutr Soc. 2016;75(4):487–500. Palla L, Almoosawi S. Diurnal patterns of energy intake derived via principal component analysis and their relationship with adiposity measures in adolescents: Results from the national diet and nutrition survey RP (2008-2012). Nutrients. 2019;11(2):1–12. Maukonen M, Kanerva N, Partonen T, Kronholm E, Wennman H, Männistö S. The associations between chronotype, a healthy diet and obesity. Chronobiol Int. 2016;May. Xiao Q, Garaulet M, Scheer FAJL. Meal timing and obesity; interactions with macronutrient intake and chronotype. Int J Obes (Lond). 2019;344(6188):1173–8. Roenneberg T, Allebrandt K v., Merrow M, Vetter C. Social jetlag and obesity. Current Biology. 2012;22(10):939–43. Bennette C, Vickers A. Against quantiles: categorization of continuous variables in epidemiologic research, and its discontents. BMC Med Res Methodol. 2012 Dec 29;12(1):21. Royston P, Altman DG, Sauerbrei W. Dichotomizing continuous predictors in multiple regression: a bad idea. Stat Med. 2006 Jan 15;25(1):127–41. Carroll RJ, Midthune D, Subar AF, Shumakovich M, Freedman LS, Thompson FE, et al. Taking advantage of the strengths of 2 different dietary assessment instruments to improve intake estimates for nutritional epidemiology. Am J Epidemiol. 2012 Feb 15;175(4):340–7. Tables Tables 1 to 5 are available in the Supplementary Files section Additional Declarations Competing interest reported. J.D.J. has collaborated with Nestlé and has undertaken consultancy work for Kellogg’s and International Flavors and Fragrances (IFF). A.F. is a director of Alinea Nutrition Ltd. and declares no non-financial competing interests. Supplementary Files Flanaganetal.SupplementaryData.docx Tables.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 23 Jun, 2025 Reviews received at journal 06 Jun, 2025 Reviewers agreed at journal 26 May, 2025 Reviews received at journal 23 May, 2025 Reviewers agreed at journal 23 May, 2025 Reviewers invited by journal 22 May, 2025 Editor assigned by journal 19 May, 2025 Submission checks completed at journal 19 May, 2025 First submitted to journal 14 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6662028","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":461424496,"identity":"807b04e5-f21d-46f7-8f27-ca2ae415c50b","order_by":0,"name":"Alan Flanagan","email":"data:image/png;base64,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","orcid":"","institution":"University of Surrey","correspondingAuthor":true,"prefix":"","firstName":"Alan","middleName":"","lastName":"Flanagan","suffix":""},{"id":461424497,"identity":"42b92a74-8c2b-4425-a5f1-e9a8c740a8a0","order_by":1,"name":"Barbara A Fielding","email":"","orcid":"","institution":"University of Surrey","correspondingAuthor":false,"prefix":"","firstName":"Barbara","middleName":"A","lastName":"Fielding","suffix":""},{"id":461424498,"identity":"4df86e62-4dc8-4d7b-87ae-8a8703d819a8","order_by":2,"name":"Jonathan D Johnston","email":"","orcid":"","institution":"University of Surrey","correspondingAuthor":false,"prefix":"","firstName":"Jonathan","middleName":"D","lastName":"Johnston","suffix":""},{"id":461424499,"identity":"f5eb11f6-cbb1-4fba-bbda-117788db3664","order_by":3,"name":"Eva Winnebeck","email":"","orcid":"","institution":"University of Surrey","correspondingAuthor":false,"prefix":"","firstName":"Eva","middleName":"","lastName":"Winnebeck","suffix":""}],"badges":[],"createdAt":"2025-05-14 08:23:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6662028/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6662028/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83480131,"identity":"f35c5ee8-d672-45a8-b948-3583face257c","added_by":"auto","created_at":"2025-05-27 06:29:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":210984,"visible":true,"origin":"","legend":"\u003cp\u003eModel estimated marginal means for energy intake (Kilocalories [kcal]) consumed on \u003cstrong\u003eA\u003c/strong\u003e. weekdays and \u003cstrong\u003eB\u003c/strong\u003e. weekends in male (blue) and female (red) participants across different time-bins of the day comparing \u0026lt;1 hour (open symbols, dashed line) to \u0026gt;1 hour (closed symbols, solid line) of social jetlag (SJL). \u003cstrong\u003eA\u003c/strong\u003e. ***\u003cem\u003e p \u003c/em\u003e\u0026lt; 0.001, **\u003cem\u003e p \u003c/em\u003e\u0026lt; 0.01, and *\u003cem\u003e p \u003c/em\u003e\u0026lt; 0.05, for within-sex and time-bin post-hoc comparisons of energy intake between \u0026lt;1 h SJL or \u0026gt;1 h SJL in females on weekdays in the early morning evening (04:00–08:00 h), early evening (16:00–20:00 h) and late evening (20:00–00:00 h) and on \u003cstrong\u003eB\u003c/strong\u003e. ## \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.01 for within-sex and time-bin post-hoc comparisons of energy intake between \u0026lt;1 h SJL or \u0026gt;1 h SJL in males in the afternoon (12:00–16:00 h) and *\u003cem\u003e p \u003c/em\u003e\u0026lt; 0.05 for females in the late morning (08:00–12:00 h). All data is presented as mean and standard error. Marginal means were calculated for females and males with average sample age and BMI; see Methods for further reference values.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6662028/v1/6c494511dcd0f19ca9f21df9.png"},{"id":83480134,"identity":"97dc9782-bfe1-4ed4-8622-224d76a7492e","added_by":"auto","created_at":"2025-05-27 06:29:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":425183,"visible":true,"origin":"","legend":"\u003cp\u003eModel estimated marginal means for energy intake (Kilocalories [kcal]) per increasing hour of social jetlag (SJL) from 0.5 h SJL (circles), 1.0 h SJL (squares), 2.0 h SJL (triangles), and 3.0 h SJL (rectangles) in male (blue lines) and female (red line) participants \u003cstrong\u003eA\u003c/strong\u003e. male participants on weekdays and \u003cstrong\u003eB\u003c/strong\u003e. female participants on weekdays, and \u003cstrong\u003eC\u003c/strong\u003e. male participants on weekends and \u003cstrong\u003eD\u003c/strong\u003e. female participants on weekends. \u003cstrong\u003e***\u003c/strong\u003e\u003cem\u003e p \u003c/em\u003e\u0026lt; 0.001, ** \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.01 and * \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.5 for females and \u003cstrong\u003e###\u003c/strong\u003e\u003cem\u003e p \u003c/em\u003e\u0026lt; 0.001, ## \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.01 and # \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.5 for males, indicating significant effects of SJL on energy intake within female and within male participants at a particular time bin. All data is presented as mean and standard error. Marginal means were calculated for females and males with average sample age and BMI; see Methods for further reference values. Note the differing y-axis scaling between energy intake in females and males.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6662028/v1/19049a54a53ab74abae48624.png"},{"id":83480896,"identity":"29948adc-23d8-4830-8683-9ae4ca0d5ab8","added_by":"auto","created_at":"2025-05-27 06:45:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1304081,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6662028/v1/44103072-0b8f-4136-a13e-a228531f6129.pdf"},{"id":83480372,"identity":"e2a42454-10e9-4a79-9b02-28d2847e7080","added_by":"auto","created_at":"2025-05-27 06:37:15","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":540309,"visible":true,"origin":"","legend":"","description":"","filename":"Flanaganetal.SupplementaryData.docx","url":"https://assets-eu.researchsquare.com/files/rs-6662028/v1/dae1e12d41599d2ea2de65fa.docx"},{"id":83480129,"identity":"db6f6190-4825-4308-8fc9-7cd278220501","added_by":"auto","created_at":"2025-05-27 06:29:15","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":38348,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6662028/v1/2a25ba1e13039c1b59f75e29.docx"}],"financialInterests":"Competing interest reported. J.D.J. has collaborated with Nestlé and has undertaken consultancy work for Kellogg’s and International Flavors and Fragrances (IFF). A.F. is a director of Alinea Nutrition Ltd. and declares no non-financial competing interests.","formattedTitle":"Social jetlag predicts greater evening energy intake in a UK cohort","fulltext":[{"header":"Introduction","content":"\u003cp\u003e‘Circadian rhythms’ are biological rhythms that are endogenously generated with a period length of approximately 24 h, and entrain to external environmental time cues\u0026nbsp;(1,2). These rhythms modulate most aspects of physiology, including metabolism and responses to food (2–4)\u0026nbsp;Circadian entrainment is achieved when external time cues are aligned with internal clock timing and continually reinforced through the timing of behaviours and environmental signals (1). Circadian misalignment occurs where the timing of environmental time-cues [e.g., the light-dark cycle] and/or behavioural cycles [e.g., sleep/wake and/or feeding/fasting] are misaligned with the phase angle of endogenous SCN rhythms (5).\u003c/p\u003e\n\u003cp\u003eOne emerging concept that represents a form of circadian misalignment is ‘social jetlag’ (SJL).\u0026nbsp;SJL describes the differences between sleep timing on work days, with enforced wake times, and free days or weekends, creating a discordance between internal biological time and social timing that can lead to a chronic form of jetlag (6). ‘Chronotype’ denotes an individual’s behavioural expression of sleep-wake timing preferences, expressed as a continuous variable ranging from early ‘morning larks’ to late ‘night owls’, related to the phase of their internal biological time (7). To date, an evening chronotype has been associated with adverse health behaviours and outcomes (8,9). It has previously been shown that chronotype-specific sleep onset exists on workdays, despite similar social clock wake times, indicating that the social clock is not strong enough to advance the circadian clock or phase of sleep (7). Therefore, evening chronotypes accumulate more sleep debt during the week, while morning types suffer the opposite on weekends/free days, due to social pressure to stay up later (7).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSeveral authors have posited that the relationship between chronotype, social behaviours, and timing of food intake may be mediated by SJL (6,7,10,11). SJL of \u0026gt; 2 h has been associated with higher cardiometabolic risk factors, including inflammatory markers, fasting glucose, waist circumference, and higher odds for obesity and metabolic syndrome (10,12). SJL\u0026nbsp;may exert negative influences on self-regulation and health-promoting behaviours, influencing dietary intake and health status (13). Whether the adverse associations between SJL and metabolic health may reflect biological factors, i.e., rhythms in glucose metabolism (14,15)\u0026nbsp; \u0026nbsp;or social factors, i.e., sleep debt and delayed meal timing (16,17), is unclear. However, it indicates that behavioural influences on meal timing may have an inherent time-of-day preference component.\u003c/p\u003e\n\u003cp\u003eTo date, the\u0026nbsp;majority of research in relation to SJL comprises cross-sectional studies (10,12,16,17).\u0026nbsp;The primary aim of the present study was to longitudinally investigate the relationship between SJL and energy intake, between weekdays and weekends. Secondary aims included investigating the associations between SJL and meal timing between weekdays and weekends. We hypothesised that higher SJL levels would be associated with greater energy intake later in the day.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Population\u003c/h2\u003e \u003cp\u003eA longitudinal, observational study was conducted over 14 days, designed to capture two full working weeks and two full weekends. A favourable ethical opinion for the study was obtained from the University of Surrey Ethics Committee [FHMS 20\u0026ndash;21 096 EGA], and written informed consent was obtained from all participants before entry into the study. Eligibility criteria comprised of healthy adult male and female participants aged between 25\u0026ndash;50 y in full-time occupations, no current night-shift work, no sleep disorders or sleep-enhancing nutrition/herbal supplements (e.g., melatonin), and \u0026lt;\u0026thinsp;14 units alcohol per week. Recruitment was conducted through online adverts on social media and email. 110 participants completed screening, of which eight withdrew before or during the beginning of the diet data collection period; one further participant was excluded due to incomplete diet diaries. The final sample analysed here included 101 participants. The study ran in four separate cohorts between April to September 2021.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDietary Assessment\u003c/h3\u003e\n\u003cp\u003eDietary intake was tracked using a photographic mobile phone application, \u0026lsquo;\u003cem\u003eSee How You Eat Food Diary App\u003c/em\u003e\u0026rsquo; (Health Revolution Ltd., Kotka, Finland). This app allowed participants to photographically record all food and beverages consumed, time-stamped with the time of day the photograph was taken and allowed for further detailed written descriptions of the meal contents to be provided. Participants exported each day\u0026rsquo;s diet diary, inclusive of photographs and meal descriptions, to be emailed to the research team. To be included in the final analysis, compliance was deemed to be a minimum of 2 diet entries per day, on 12 of the 14 days. Each study cohort began on a Monday and ran through 14 days inclusive of two weekends. 91% of the final analytical sample completed a full 14 days of dietary recording.\u003c/p\u003e \u003cp\u003eDietary intake data were analysed using \u003cem\u003eNutritics\u003c/em\u003e (v5.029, Dublin, Ireland), with the 2015 \u0026lsquo;Composition of Foods Integrated Dataset \u0026lsquo;CoFID\u0026rsquo;, including McCance and Widdowson\u0026rsquo;s 7th Edition, utilised as the relevant database for diet diary entries. To account for diary entries failing to specify a portion size [i.e. in cups, grams, or spoon measures, or readily identifiable pre-packaged portions], the demographic average portion sizes, based on the UK National Diet and Nutrition Survey for age and sex-specific reported portion sizes was entered for a given food item or beverage (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). To be classified as an eating occasion, a minimum energy criterion of 50kcal was applied (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe validity of self-reported dietary intake was assessed by testing the agreement between energy intake reported through the app and estimated energy requirements (EER) as the reference measure. Reported energy intake (REI) was calculated as the mean total daily energy for all reported days of dietary intake. EER was estimated for each participant using the Henry-Oxford 2005 equation, factoring in age, height, weight, and reported physical activity level (PAL), where estimated resting metabolic rate (RMR) was multiplied by the PAL (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). A Bland-Altman comparison was used to test the agreement between average self-reported total daily energy intake over the 14 days of dietary recording and EER. The mean of the differences was \u0026minus;\u0026thinsp;277 kcal (95% CI of the Mean \u0026minus;\u0026thinsp;368 to \u0026minus;\u0026thinsp;186 kcal), and 95% Lower to Upper Limits of Agreement of \u0026minus;\u0026thinsp;1179 to 624 kcal (\u003cb\u003eSupplementary Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e \u0026amp; S2; Suppl. Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). The narrow confidence intervals around the mean difference, i.e., the systematic error, and the limits of agreement largely within \u0026plusmn;\u0026thinsp;1,200 kcal, suggest lower individual-level error, indicating that the data is suitable for group-level analysis (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eAssessment of Chronotype and Social Jetlag\u003c/h3\u003e\n\u003cp\u003eChronotype was determined using the validated Munich Chronotype Questionnaire (MCTQ), as previously described (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Chronotype was calculated as the midpoint of sleep on free days (i.e., weekends; MSF), where SOf is sleep onset on free days and SDf is sleep duration on free days (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e):\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:MSF\\:=\\:SOf\\:+\\:\\frac{SDf}{2}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhich was corrected for oversleeping in participants who slept longer on weekends compared to weekdays using the equation from Roenneberg et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e):\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:MSFsc=MSF\\:-\\:\\frac{\\left(SDf-SDweek\\right)}{2}\\:=SOf\\:+\\:\\frac{SDweek}{2}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003eMSF\u003c/em\u003e is the midpoint of sleep on free days, \u003cem\u003eMSFsc\u003c/em\u003e is the midpoint of sleep corrected for sleep debt on free days, \u003cem\u003eSDweek\u003c/em\u003e is the average weekly sleep duration, \u003cem\u003eSOf\u003c/em\u003e is sleep onset on free days, and \u003cem\u003eSDf\u003c/em\u003e is sleep duration on free days. MSFsc has been shown to correlate strongly [\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.68] with the dim-light melatonin onset [DLMO], an objective laboratory measure of internal circadian phase (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSocial jetlag [SJL] was also determined using the MCTQ (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), and calculated as the difference between the midpoint of sleep on workdays and weekends (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e):\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:SJL=MSF-MSW\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003eMSF\u003c/em\u003e is the midpoint of sleep on free days and \u003cem\u003eMSW\u003c/em\u003e is the midpoint of sleep on workdays. Distributions of SJL and chronotype in the study cohort are presented in \u003cb\u003eSuppl. Figures S3 \u0026amp; S4\u003c/b\u003e.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eCovariates including age, sex, height, weight, calculated body mass index (BMI; weight [kg] / height squared [meters\u003csup\u003e2\u003c/sup\u003e]), work location, dependent children in the home, smoking status, and weekly alcohol intake, were collected using a self-reported baseline screening questionnaire and included in the statistical models.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eCategorical variables were summarised as number and percentages, while continuous variables were summarised as mean and standard deviation. Energy intake was separated in 4-h time-bins: 04:00\u0026ndash;08:00 h (\u0026lsquo;Early Morning\u0026rsquo;); 08:00\u0026ndash;12:00 h (\u0026lsquo;Late Morning\u0026rsquo;); 12:00\u0026ndash;16:00 h (\u0026lsquo;Afternoon\u0026rsquo;); 16:00\u0026ndash;20:00 h (\u0026lsquo;Early Evening\u0026rsquo;), and 20:00\u0026ndash;00:00 h (\u0026lsquo;Late Evening\u0026rsquo;), and for two conditions, \u0026lsquo;Weekday\u0026rsquo; and \u0026lsquo;Weekend\u0026rsquo;, for each time-bin. The primary exposure of interest was SJL. The primary outcomes of interest were overall energy intake on workdays and weekends.\u003c/p\u003e \u003cp\u003eData were analysed using random-effects linear mixed models to assess differences in temporal energy intake longitudinally over 14 days. Models were fitted using restricted maximum likelihood to all observed energy intake data for each of the 101 participants included in the final analysis. Each model consisted of a random intercept for individual participants to account for within-participant correlations in the observed longitudinal data. The models contained fixed effects for SJL and chronotype, in addition to the continuous variables of mean-centred age, BMI, and alcohol intake, and categorical variables including sex (reference level: female), work location (reference level: external job site), dependent children (yes/no), and smoking status (reference level: never), as covariates. Covariates were determined on a theoretical basis according to the wider literature related to factors which may influence sleep-wake cycles, chronotype, and related dietary behaviours (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). The models allowed for testing differences in energy intake according to time-bin, SJL, and sex of participants, including their two- and three-way interactions. Models were further analysed and visualised using estimated marginal means and post-hoc pairwise comparisons with Tukey correction for multiple comparisons.\u003c/p\u003e \u003cp\u003eWe generated three separate models in our data analysis. Across these, SJL was modelled both as a categorical and continuous variable.\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIn Model 1, SJL was categorised as \u0026lt;\u0026thinsp;1 h or \u0026gt;\u0026thinsp;1 h, approximate to the mean SJL level of 58 min or 0.97 h in the participants and consistent with previous research examining diet and SJL (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIn Model 2, SJL was treated as a continuous variable to investigate the influence of categorisation on the results.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIn Model 3, sensitivity analyses were conducted by trichotomising SJL in distribution-based interquartile ranges of top 25% [\u0026gt;\u0026thinsp;75 mins], moderate 50% [27\u0026ndash;75 mins], and low 25% [\u0026lt;\u0026thinsp;27 mins] SJL.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eWe also conducted sensitivity analyses on Model 1 by excluding chronotype from the models to determine whether chronotype influenced any associations observed for SJL.\u003c/p\u003e \u003cp\u003eAll data were analysed using the \u003cem\u003elmer\u003c/em\u003e and \u003cem\u003eemmeans\u003c/em\u003e packages in \u003cem\u003eR\u003c/em\u003e v4.3.2 (R Core Team, The R Foundation for Statistical Computing, 2023), and graphing was conducted using GraphPad Prism v9.3.1 (GraphPad Software 2021, La Jolla California, USA). Outcomes are presented as Beta (unstandardised) coefficients with 95% confidence intervals (CI) and conditional \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e, the latter of which considers both fixed and random effects in the model.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eDemographics and descriptive statistics for the cohort can be found in \u003cstrong\u003eTable 1\u003c/strong\u003e. The study sample comprised 77.5% females and 22.5% males with a mean age of 32.7 (\u0026plusmn; 6.5yrs), and mean BMI of 23.7 kg/m\u003csup\u003e2\u003c/sup\u003e (\u0026plusmn; 3.7). Mean SJL was 58 min (\u0026plusmn; 41 min; range 0-203 min), while mean chronotype (as MSF\u003csub\u003eSC\u003c/sub\u003e) was 03:25 h (\u0026plusmn; 00:54 h; range 01:26 \u0026ndash; 06:10 h).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAssociations Between Energy Intake and \u0026lt; 1 h vs. \u0026gt; 1 h Social Jetlag (Model 1)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 1 displays results based on marginal means derived from the linear mixed models, while full model coefficients, 95% CI, and \u003cem\u003eP\u003c/em\u003e-values may be found in \u003cstrong\u003eTable 2.\u003c/strong\u003e Results of post-hoc comparisons are listed in \u003cstrong\u003eTable 3\u003c/strong\u003e.The linear mixed model for weekdays explained 23.9% and for weekends 26.9% of the variance in energy intake (\u003cstrong\u003eTable 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eOverall, energy intake increased sequentially from before 08:00 h (the reference category) to the 16:00\u0026ndash;20:00 h time bin, before declining in the 20:00\u0026ndash;00:00 h time bin on both weekdays and weekends. This temporal pattern was modulated by sex, whereby male participants exhibited a greater peak in energy intake, particularly in the evening (16:00-20:00 h) during both weekdays and weekends (\u003cstrong\u003eFigure 1\u003c/strong\u003e). Importantly, SJL also modulated this temporal pattern but with different effects in females and males. In female participants, the overall pattern indicated greater energy intake earlier in the day in the lower SJL group than in the higher SJL group. Although effect sizes and statistical significances in post-hoc pairwise comparisons varied between weekday and weekend models, the pattern was qualitatively similar between both day types (\u003cstrong\u003eFig.1, Tables 2 \u0026amp; 3\u003c/strong\u003e). On weekdays, post-hoc pairwise comparisons indicated statistically significantly lower energy intake in female participants with \u0026gt;1 h SJL than those with \u0026lt;1 h SJL both in the early morning (04:00\u0026ndash;08:00 h) and the early evening (16:00-20:00) by 81 kcal (95% CI, \u0026ndash;141 to \u0026ndash;22 kcal) and 70 kcal (\u0026ndash;130 to \u0026ndash;11 kcal), respectively (\u003cstrong\u003eFig. 1, Table 3\u003c/strong\u003e). Conversely, energy intake was higher in the late evening (20:00-00:00) by 100 kcal (41 to 160 kcal) for females in the \u0026gt;1h SJL category. On weekends, post-hoc comparisons only indicated a statistically significant difference in the late morning (08:00\u0026ndash;12:00 h), where females with \u0026gt; 1 h SJL exhibited 101 kcal (\u0026ndash;185 to \u0026ndash;16 kcal) lower energy intake compared to females with \u0026lt; 1 h SJL. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBy contrast, in male participants, who constituted only a small proportion of the overall sample, SJL appeared to be only associated with temporal energy intake in the afternoon, most predominantly on weekends. In males with \u0026gt; 1 h SJL, post-hoc pairwise comparisons indicated that energy intake on weekend afternoons (12:00\u0026ndash;16:00 h) was 249 kcal (\u0026ndash;405 to \u0026ndash;93 kcal) lower compared to males with \u0026lt;1 h SJL (Table 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall, the results indicate that temporal energy intake varied based on both SJL level and participants\u0026apos; sex, which interacted to influence temporal patterns of energy intake in our sample.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAssociations Between Energy Intake and Social Jetlag as a Continuous Variable (Model 2)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eModelling SJL as a continuous variable showed similar results to the categorical models and explained 24.0% of the variance in energy intake on weekdays and 27.0% on weekends (\u003cstrong\u003eTables 4 and 5\u003c/strong\u003e). Marginal means plots can be found in \u003cstrong\u003eFigure 2\u003c/strong\u003e, while full model coefficients, 95% CI, and \u003cem\u003eP\u003c/em\u003e-values, may be found in \u003cstrong\u003eTables 4 and 5\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eOverall, for the larger sample of female participants, the models indicated a clear temporal pattern between SJL and energy intake, matching the findings from the dichotomous model: the higher the SJL, the lower the energy intake in the morning and the higher in the late evening. On weekdays in females, each hour increase in SJL was associated with 51 kcal (\u0026ndash;99 to \u0026ndash;3 kcal) less energy intake between 08:00\u0026ndash;12:00 h and with 66 kcal (18 to 114 kcal) greater energy intake in the late evening between 20:00\u0026ndash;00:00 h (\u003cstrong\u003eFigure 2\u003c/strong\u003e). In female participants on weekends, a similar temporal pattern was evident: between 08:00\u0026ndash;12:00 h, each hour increase in SJL was associated with 72 kcal (\u0026ndash;141 to \u0026ndash;2 kcal) less energy intake, while between 20:00\u0026ndash;00:00 h, with 61 kcal (\u0026ndash;8 to 131 kcal) greater energy intake.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe pattern in the smaller male sample was more varied than the categorical analysis. Consistent with the dichotomous model (Model 1) was the effect of SJL on energy intake in the early afternoon. In the afternoons between 12:00\u0026ndash;16:00 h, each hour increase in SJL was associated with 102 kcal (\u0026ndash;174 to \u0026ndash;30 kcal) less energy intake on weekdays and 130 kcal (\u0026ndash;233 to \u0026ndash;26 kcal) less on weekends. However, each hour increase in SJL was associated with 141 kcal (70 to 213 kcal) greater energy intake between 08:00\u0026ndash;12:00 h, which was in the opposite direction to females at this time of day. In the early evening period between 16:00\u0026ndash;20:00 h, each hour increase in SJL was similarly associated with 148 kcal (76 to 219 kcal) greater energy intake. On weekends, it was the latest time point (20:00\u0026ndash;00:00 h) where males showed greater energy intake of 99 kcal (\u0026ndash;5 to 202 kcal) with increasing SJL, similar to the pattern observed in female participants at this time of day.\u003c/p\u003e\n\u003cp\u003eOverall, the results of the continuous analysis similarly indicate a modification of energy intake by time, sex and SJL, suggesting that increasing SJL levels differentially associate with energy intake at different times of day in male and female participants, although greater energy intake in the evening with increasing SJL was observed for both sexes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSensitivity Analyses\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSensitivity analyses with SJL trichotomized based on sample distribution as low, moderate and high levels (Model 3), generally corroborated the differences of energy intake with SJL in females observed in the dichotomous model (Model 1). Although overall slightly less congruent owing to lower sample sizes per group, Model 3 yielded additional granularity on the higher energy intake by females in the late evening, indicating that it was driven by participants in the top 25% of SJL levels (\u003cstrong\u003eSupp. Table S2; Figure S5\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn a final sensitivity analysis, chronotype was removed as a fixed effect from Model 1 and the results of the main analyses remained essentially unchanged (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e, 0.239 for weekday energy intake and \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e, 0.269 for weekend energy intake) (\u003cstrong\u003eSupp. Table S3\u003c/strong\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSJL represents an emerging risk factor that exhibits a high prevalence in the general population, with estimates of up to 46% of the population having\u0026thinsp;\u0026gt;\u0026thinsp;1 h SJL (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Unlike the adaptations to a new time zone that occur with trans-meridian jetlag, SJL represents a more chronic form of circadian misalignment and therefore, as a conceptual exposure, may be more akin to shift work, i.e., a discordance between internal biological timing and external social timing, characterised as a chronic form of jetlag (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The discordance between biological timekeeping and the social clock may exert a strain on the circadian system, which accumulates into adverse metabolic risk associated with greater SJL levels (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). However, the degree to which these associations may relate to circadian disruption, dietary factors, or an interrelationship of both remains to be fully elucidated. Furthermore, the majority of research on SJL and diet comprises cross-sectional studies (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). As such, the direction of effect, i.e., whether differences in SJL relate to true differences in patterns of energy intake or diet quality that may precipitate higher metabolic disease risk, remains unclear.\u003c/p\u003e \u003cp\u003eIn this study, our primary exposure of interest was absolute energy intake, with anticipated sex differences due to sex dimorphism in body size and composition (i.e., lean mass and fat mass). All participants displayed a temporal pattern of increasing energy intake throughout the day, peaking in the early evening [16:00\u0026ndash;20:00 h], which aligns with the temporal energy intake patterns previously observed in UK populations (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Our data suggest that SJL may exert a moderating influence on temporal energy intake patterns that differ by sex. However, the magnitude of the effect estimates was modest and, in caloric terms, unlikely to significantly explain the higher risks of obesity and metabolic diseases previously associated with SJL (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Previous research analysing the association between SJL, categorised dichotomously as in our current study, and dietary intake, has revealed statistically significant, but small magnitude of differences in total and saturated fat intake, dietary cholesterol, servings of beans, and sweets (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Therefore, the extent to which the adverse metabolic consequences of heightened SJL relate to diet or other lifestyle factors remains to be determined.\u003c/p\u003e \u003cp\u003eA late chronotype has been associated, to date, with lower diet quality and a redistribution of energy and macronutrient intake to later in the day (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Previous research has suggested that associations between the timing of energy intake and overweight/obesity may be dependent on chronotype (37,38). In addition, several studies have suggested that the relationship between chronotype and diet may be mediated by SJL (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). This may be because higher SJL levels tend to correlate strongly with late chronotypes (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). On average, intermediate chronotypes with an average midpoint of sleep of 03:00 h exhibit the least SJL, as they have the least differences in sleep timing and duration between work and free days (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). In our analysis, removing chronotype from Model resulted in no change in the model estimates, suggesting that chronotype had little influence on the associations between SJL and energy intake. However, in our participants, the mean midpoint of sleep was 03:25 h (\u0026plusmn;\u0026thinsp;00:54), in a range where minimal SJL would be expected. To what extent SJL and chronotype interact to influence associations with diet and metabolic health remains to be elucidated and will require larger sample sizes in well-conducted prospective cohort studies to determine.\u003c/p\u003e \u003cp\u003eImportantly, the choice of categorisation for SJL as an exposure may influence any observed associations. We categorised SJL based on previous research categorising SJL dichotomously as over or under 1 hour (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) or in tertiles with hourly increments (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). However, arbitrary categorisation may result in a loss of power and the potential bias of not accurately capturing variation within the ranges of each category (39,40). Although the overall variance explained by both our categorical and continuous models was similar, modelling SJL as a continuous variable suggested an interaction between increasing SJL levels and sex on temporal energy intake that was not evident when SJL was categorised dichotomously as \u0026lt;\u0026thinsp;1 h or \u0026gt;\u0026thinsp;1 h weekly SJL. To date, we are unaware of other nutrition research which has modelled SJL as a continuous variable, and given the emerging nature of SJL as a risk factor, future research should consider this approach to investigating associations between SJL, diet, and metabolic risk.\u003c/p\u003e \u003cp\u003eOur study has several strengths and limitations. The primary strength of the study is utilising all available data on energy intake in participants from 14 days of longitudinal dietary data collection, rather than mean energy intake for each time point, to fully capture potential variance in the linear models. The study period also captured two full working weeks and two full weekends and thus, the variations observed may be more representative and may have minimised within-person variation that could arise with only one week of data. We adjusted for covariates which could be expected to influence circadian rhythms and/or sleep cycles, including workplace, dependent children, and alcohol intake. Further, the level of compliance with dietary recording was excellent. We used well-validated questionnaires to assess chronotype and social jetlag. However, limitations of the present analysis should be noted.. These include the observational design and potential for unmeasured covariates to influence the observed associations, the small sample size, and self-reported energy intake that indicated the presence of systematic bias and underestimation of energy intake, which is the expected direction of effect with self-reported dietary data (41). In addition, the use of the smartphone application method of dietary assessment was unvalidated against a reference measure of dietary intake. The study sample was predominantly female, and outcomes in the categorical models primarily reflected the influence of energy intake in female participants. Our participants were also primarily recruited through social media platforms and exhibited high levels of a range of health-promoting behaviours. Thus, our sample may not be fully representative of the general population.\u003c/p\u003e \u003cp\u003eIn conclusion, our data suggest that higher weekly levels of SJL may be associated with greater energy intake in the evening and nighttime. However, the magnitude of the effect estimates was modest and, in caloric terms, unlikely to explain the higher risks of obesity and metabolic diseases previously associated with increasing SJL levels. Future larger prospective and intervention studies could shed additional light on the potential relevance of SJL, irregular temporal eating patterns, and metabolic risk.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the participants for taking part in the study. This study received no funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualisation, A.F., B.A.F., J.D.J.; Methodology, A.F., B.A.F., J.D.J., E.W.; Formal Analysis, A.F., E.W.; Writing – Original Draft, A.F.; Review \u0026amp; Editing, all authors. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.D.J. has collaborated with Nestlé and has undertaken consultancy work for Kellogg’s and International Flavors and Fragrances (IFF). A.F. is a director of Alinea Nutrition Ltd. and declares no non-financial competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study are not publicly available but are available from the corresponding author on reasonable request.\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eRoenneberg T, Daan S, Merrow M. The Art of Entrainment. J Biol Rhythms. 2003;18(3):183\u0026ndash;94.\u003c/li\u003e\n \u003cli\u003eFlanagan A, Bechtold DA, Pot GK, Johnston JD. Chrono-nutrition: From molecular and neuronal mechanisms to human epidemiology and timed feeding patterns. J Neurochem. 2021;157(1):53\u0026ndash;72.\u003c/li\u003e\n \u003cli\u003eRuddick-Collins LC, Morgan PJ, Johnstone AM. 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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 Aug 20;32(4):359\u0026ndash;68.\u003c/li\u003e\n \u003cli\u003eReeves S, Halsey LG, McMeel Y, Huber JW. Breakfast habits, beliefs and measures of health and wellbeing in a nationally representative UK sample. Appetite. 2013;60(1):51\u0026ndash;7.\u003c/li\u003e\n \u003cli\u003eMorris CJ, Yang JN, Garcia JI, Myers S, Bozzi I, Wang W, et al. Endogenous circadian system and circadian misalignment impact glucose tolerance via separate mechanisms in humans. PNAS. 2015;112(17):E2225\u0026ndash;34.\u003c/li\u003e\n \u003cli\u003eWehrens SMT, Christou S, Isherwood C, Middleton B, Gibbs MA, Archer SN, et al. Meal Timing Regulates the Human Circadian System. Curr Biol. 2017;27(12):1768-1775.e3.\u003c/li\u003e\n \u003cli\u003eSilva CM, Mota MC, Miranda MT, Paim SL, Waterhouse J, Crispim CA. Chronotype, social jetlag and sleep debt are associated with dietary intake among Brazilian undergraduate students. Chronobiol Int. 2016;33(6):740\u0026ndash;8.\u003c/li\u003e\n \u003cli\u003eMota MC, Silva CM, Balieiro LCT, Gon\u0026ccedil;alves BF, Fahmy WM, Crispim CA. Association between social jetlag food consumption and meal times in patients with obesity-related chronic diseases. PLoS One. 2019;14(2):1\u0026ndash;14.\u003c/li\u003e\n \u003cli\u003eWrieden WL, Barton KL. Calculation and Collation of Typical Food Portion Sizes for Adults Aged 19-64 and Older People Aged 65 and Over. 2006.\u003c/li\u003e\n \u003cli\u003eWrieden WL, Longbottom PJ, Adamson AJ, Ogston SA, Payne A, Haleem MA, et al. Estimation of typical food portion sizes for children of different ages in Great Britain. British Journal of Nutrition. 2008;99(6):1344\u0026ndash;53.\u003c/li\u003e\n \u003cli\u003eLeech RM, Worsley A, Timperio A, McNaughton SA. Understanding meal patterns: Definitions, methodology and impact on nutrient intake and diet quality. Nutr Res Rev. 2015;28(1):1\u0026ndash;21.\u003c/li\u003e\n \u003cli\u003eHenry C. Basal metabolic rate studies in humans: measurement and development of new equations. Public Health Nutr. 2005 Oct 2;8(7a):1133\u0026ndash;52.\u003c/li\u003e\n \u003cli\u003ePendergast FJ, Ridgers ND, Worsley A, McNaughton SA. Evaluation of a smartphone food diary application using objectively measured energy expenditure. Int J Behav Nutr Phys Act. 2017 Dec 14;14(1):30.\u003c/li\u003e\n \u003cli\u003eRoenneberg, Pilz, Zerbini, Winnebeck. Chronotype and Social Jetlag: A (Self-) Critical Review. Biology (Basel). 2019;8(3):54.\u003c/li\u003e\n \u003cli\u003eKantermann T, Sung H, Burgess HJ. Comparing the Morningness-Eveningness Questionnaire and Munich ChronoType Questionnaire to the Dim Light Melatonin Onset. J Biol Rhythms. 2015 Oct 4;30(5):449\u0026ndash;53.\u003c/li\u003e\n \u003cli\u003eBerge JM, Truesdale KP, Sherwood NE, Mitchell N, Heerman WJ, Barkin S, et al. Beyond the dinner table: who\u0026rsquo;s having breakfast, lunch and dinner family meals and which meals are associated with better diet quality and BMI in pre-school children? Public Health Nutr. 2017 Dec 14;20(18):3275\u0026ndash;84.\u003c/li\u003e\n \u003cli\u003eMota MC, Silva CM, Balieiro LCT, Fahmy WM, Crispim CA. Social jetlag and metabolic control in non-communicable chronic diseases: A study addressing different obesity statuses. Sci Rep. 2017;7(1):1\u0026ndash;8.\u003c/li\u003e\n \u003cli\u003eAlmoosawi S, Vingeliene S, Karagounis LG, Pot GK. Chrono-nutrition: A review of current evidence from observational studies on global trends in time-of-day of energy intake and its association with obesity. Proc Nutr Soc. 2016;75(4):487\u0026ndash;500.\u003c/li\u003e\n \u003cli\u003ePalla L, Almoosawi S. Diurnal patterns of energy intake derived via principal component analysis and their relationship with adiposity measures in adolescents: Results from the national diet and nutrition survey RP (2008-2012). Nutrients. 2019;11(2):1\u0026ndash;12.\u003c/li\u003e\n \u003cli\u003eMaukonen M, Kanerva N, Partonen T, Kronholm E, Wennman H, M\u0026auml;nnist\u0026ouml; S. The associations between chronotype, a healthy diet and obesity. Chronobiol Int. 2016;May.\u003c/li\u003e\n \u003cli\u003eXiao Q, Garaulet M, Scheer FAJL. Meal timing and obesity; interactions with macronutrient intake and chronotype. Int J Obes (Lond). 2019;344(6188):1173\u0026ndash;8.\u003c/li\u003e\n \u003cli\u003eRoenneberg T, Allebrandt K v., Merrow M, Vetter C. Social jetlag and obesity. Current Biology. 2012;22(10):939\u0026ndash;43.\u003c/li\u003e\n \u003cli\u003eBennette C, Vickers A. Against quantiles: categorization of continuous variables in epidemiologic research, and its discontents. BMC Med Res Methodol. 2012 Dec 29;12(1):21.\u003c/li\u003e\n \u003cli\u003eRoyston P, Altman DG, Sauerbrei W. Dichotomizing continuous predictors in multiple regression: a bad idea. Stat Med. 2006 Jan 15;25(1):127\u0026ndash;41.\u003c/li\u003e\n \u003cli\u003eCarroll RJ, Midthune D, Subar AF, Shumakovich M, Freedman LS, Thompson FE, et al. Taking advantage of the strengths of 2 different dietary assessment instruments to improve intake estimates for nutritional epidemiology. Am J Epidemiol. 2012 Feb 15;175(4):340\u0026ndash;7.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 5 are available in the Supplementary Files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-biological-timing-and-sleep","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Biological Timing and Sleep](https://www.nature.com/npjbts)","snPcode":"44323","submissionUrl":"https://submission.springernature.com/new-submission/44323/3","title":"npj Biological Timing and Sleep","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Diet, nutrition, chrono-nutrition, circadian, meal timing","lastPublishedDoi":"10.21203/rs.3.rs-6662028/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6662028/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSocial jetlag (SJL) describes the differences in sleep timing between workdays and weekends. In a 14-day longitudinal observational study, we\u003cstrong\u003e \u003c/strong\u003eexplored the associations between SJL and energy intake on workdays and weekends. Healthy male and female participants residing in the UK [\u003cem\u003en \u003c/em\u003e= 101] used a smartphone app to record dietary intake continuously during the study period, encompassing two working weeks and weekends. We hypothesised that higher SJL levels would be associated with greater energy intake after 16:00 h and that any such effect would be most evident in late chronotypes. We found that increasing SJL levels modified temporal energy intake in both female and male participants, while temporal patterns of energy intake differed by sex. The modifying effect of SJL differed slightly between weekdays and weekends. Inclusion of chronotype in the statistical models did not alter the associations between SJL and energy intake. Furthermore, the magnitude of the effect estimates was modest and, in caloric terms, unlikely to explain the higher risks of obesity and metabolic diseases previously associated with increasing SJL levels.\u003c/p\u003e","manuscriptTitle":"Social jetlag predicts greater evening energy intake in a UK cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-27 06:29:10","doi":"10.21203/rs.3.rs-6662028/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-23T13:49:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-06T09:32:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"133068059827039654632179641580494818855","date":"2025-05-26T11:26:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-23T17:26:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"269135536944500366285250322206756063919","date":"2025-05-23T05:22:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-22T12:16:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-19T20:29:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-19T16:59:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Biological Timing and Sleep","date":"2025-05-14T08:19:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-biological-timing-and-sleep","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Biological Timing and Sleep](https://www.nature.com/npjbts)","snPcode":"44323","submissionUrl":"https://submission.springernature.com/new-submission/44323/3","title":"npj Biological Timing and Sleep","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"aebc8489-9ab0-46e0-9ad8-0c1d4db2b269","owner":[],"postedDate":"May 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":49006896,"name":"Biological sciences/Cell biology/Circadian rhythms"},{"id":49006897,"name":"Health sciences/Diseases"}],"tags":[],"updatedAt":"2025-06-23T13:53:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-27 06:29:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6662028","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6662028","identity":"rs-6662028","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}