Social Jetlag and Obesity Roenneberg, Till; Allebrandt, Karla V.; Merrow, Martha; Vetter, Celine

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1 University of Groningen Social Jetlag and Obesity Roenneberg, Till; Allebrandt, Karla V.; Merrow, Martha; Vetter, Celine Published in: Current Biology DOI: /j.cub IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below. Document Version Publisher's PDF, also known as Version of record Publication date: 2012 Link to publication in University of Groningen/UMCG research database Citation for published version (APA): Roenneberg, T., Allebrandt, K. V., Merrow, M., & Vetter, C. (2012). Social Jetlag and Obesity. Current Biology, 22(10), DOI: /j.cub Copyright Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons). Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Downloaded from the University of Groningen/UMCG research database (Pure): For technical reasons the number of authors shown on this cover page is limited to 10 maximum. Download date:

2 Current Biology, Volume 22 Supplemental Information Social Jetlag and Obesity Till Roenneberg, Karla V. Allebrandt, Martha Merrow, and Céline Vetter Supplemental Inventory 1. Supplemental Figures and Tables Figure S1, related to Figure 1B Figure S2 Figure S3, related to Figure 2 Table S1, related to Figure 2 Table S2, related to Supplemental Experimental Procedures Table S3, related to Figure 3 Table S4, related to Figure 2 2. Supplemental Experimental Procedures 3. Supplemental References

3 Figure S1. Social Jetlag (A) Six-week long sleep-log of an extremely late chronotype (MSF 7), exemplifying the typical scalloping between sleep time on workdays and on free days (e.g., weekends). The top horizontal axis represents local time and the vertical axis represent the days of the sleep-log. The coloured bars sow the timing and duration of sleep on the respective days (red: workdays; green: free days). The difference between the mid-sleep point on free days, MSF) and that on workdays (MSW) is used to quantify social jetlag. Note that sleep on workdays in this late type is interrupted by the alarm clock (constant sleep end at around 7 a.m., corresponding to the MSF point of this subject). Although this is an extreme example of social jetlag (due to the late chronotype in combination with an early work start), the majority of the population shows similar patterns. (B) Distribution of social jetlag (MSF-MSW) in the population. Note that early chronotypes generally sleep outside of their circadian sleep window on evenings before free days due to the social pressure of a latechronotype society that makes them go to bed after their biological bedtime (hence negative social jetlag values). The majority of the population experiences the discrepancy between biological and social time on workdays (as shown in A), leading to positive values of social jetlag. (C) Age- and sex-dependencies of social jetlag (see Evaluation of the MCTQ below). Circles represent average values of age groups for both sexes (ages 10-65: 1 year bins; >65: 5 year bins; note that the age group 75 has no entries for social jetlag). A univariate ANOVAs with age and sex as covariates shows that these two variables influence social jetlag: age, F(1, 64,107) = 13,044.64, p < , partial η2 = 0.17 and sex, F(1, 64,107)= 362,5, p < , partial η2 = Curves are polynomial fits (black: both sexes; red: females; blue: males). Vertical lines represent standard errors of the mean (±SEM; in most cases they are smaller than the respective symbols).

4 Figure S2. Age- and Sex-Dependencies of BMI Circles represent average values of age groups (1 year bins). A univariate ANOVAs with age and sex as covariates shows that these two variables influence BMI: age, F(1, 64,107)= 5,965.9, p < , partial η2 = 0.09, and sex, F(1, )= 2,229.5, p < , partial η2 = Curves are polynomial fits (black: both sexes; red: females; blue: males). Vertical lines represent standard errors of the mean (±SEM; in most cases they are smaller than the respective symbols). Inset: Distribution of BMI in the dataset used in this study (normalised for age and sex, BMI corr ). The standardised categories indicating normal, overweight and obese body weight are shown in green, pink and red, respectively. Note, that we divided the population into two groups (normal: BMI < 25; and overweight/obese: BMI 25) for analysing the relationship between social jetlag and BMI.

5 Figure S3. The Relationship between Sleep Duration and BMI on Workdays (A) and on Free Days (B) Was Assessed by Partial Correlations Controlling for Age and Sex The association between sleep duration and BMI was almost twofold higher on work days (panel A: r = ; p < ) than on free days (panel B: r = , p < , n = 64,106). Vertical lines are ±SEM of the 30-min data bins. For illustration purposes, sleep duration and BMI were corrected for age and sex (SDcorr; BMIcorr) according to the fits shown in Figures 1 and S1, see Table S3 for the fit parameters.

6 Table S1A. Predicting BMI in the Lower BMI Group (BMI < 25, n = ), Related to Figure 3 Step Predictor variables B SE β 95% CI Step 1 Constant to Age *** to Sex *** to Step 2 Constant to Age *** to Sex *** to Average sleep duration *** to Step 3 Constant to Age *** to Sex *** to Average sleep duration *** to Social Jetlag to Step 4 Constant to Age *** to Sex *** to Average sleep duration *** to Social Jetlag to Chronotype *** to This table summarises the 4-step hierarchical multiple regression model (enter method). B: unstandardised regression coefficients; SE: standard error; ß: standardised regression coefficients; CI: confidence intervals (lower to upper bound). r 2 = for Step 1, r 2 change = (F (1,43304) = 14.36), p = 0.000) for Step 2, r 2 change = (F (1,43303) = 1.88, p = 0.17) for Step 3, and r 2 change = (F (1,43302) = 12.05, p < 0.000) for Step 4.

7 Table S1B. Predicting BMI in the Higher BMI Group (BMI 25, n = ), Related to Figure 3 Step Predictor variables B SE β 95% CI Step 1 Constant to 1.50 Age *** to Sex *** to Step 2 Constant to Age *** to Sex *** to Average sleep duration *** to Step 3 Constant to 1.58 Age *** to Sex *** to Average sleep duration *** to Social Jetlag *** to Step 4 Constant to Age *** to Sex *** to Average sleep duration *** to Social Jetlag *** to Chronotype t We used a 4-step hierarchical multiple regression model (enter method). Abbreviations as in Table 2. r 2 = for Step 1, r 2 change = (F (1,20.731) = 79,49, p < 0.000) for Step 2, r 2 change = (F (1,20.730) = 21,95, p < 0.000) for Step 3, and r 2 change = (F (1,20.730) = 0,103, p < 0.000) for Step 4. Note: in case the regression model was run with a stepwise method, chronotype was deleted from the model.

8 Supplemental Experimental Procedures The Munich ChronoType Questionnaire (MCTQ) is the central instrument of an on-going internet-based study about circadian behaviour (see chronotype Users are either the general internet-using public or individuals who are invited to participate in a dedicated project. The online MCTQ database grows on average by 825 entries/month (not including dedicated projects). The MCTQ asks simple questions about peoples sleep habits when they go to bed, when they prepare for sleep, how long they take to fall asleep, when they wake up, whether wake-up is assisted by an alarm clock and when they get out of bed. These questions are asked separately for workdays and work-free days. The first assessment of chronotype is based on the midpoint of sleep on free days (MSF), which is then corrected for oversleep on free days (MSF sc ), a phenomenon reflecting compensation for accumulated sleep debt during the workweek. The detailed evaluation of the MCTQ and the computation of all derived variables is shown at the end of the SOM. BMI was computed by using the standard formula: weight (kg)/height (m) 2. We also asked how much time people spend outdoors without a roof above their head. This study is based on the database as of the end of July It contained 81,888 entries of the general internet-using public (i.e., dedicated projects were not used for the evaluations presented here). This dataset was inspected for and cleaned (see table S2) of extreme values, for individuals who have no regular week schedules (i.e., not working or having no free days during the week) as well as for those who use alarm clocks on free days (since chronotype cannot be calculated for individuals who never report sleep times without restrictions). After cleaning, the number of entries for each year are as shown in table S3. Except for the trend analyses shown in Fig. 4, the year 2002 was excluded from analyses due to the strong imbalance of entries compared to other years. Age- and sex corrections were performed by calculating the difference (in %) between the actual value and the value yielded by the sex-specific fit for the given age. This difference was then added to the value of the fit for the total population for the age of 30 years (see respective fit parameters in table S4). Table S2. Exclusion Criteria Applied to the Database and the Remaining Number of Participants in the Sample, Related to Supplemental Experimental Procedures n.i. = not indicated. Exclusion criteria Number of Exclusions Age < 16 and > 65 or n.i. 2,746 BMI <16 and > 60 or n.i. 1,813 Sleep offset on workdays > 12:00 or n.i. 1,763 Number of work days 0 or 7 2,681 Alarm on free days 8,661 MSF > 12:00 15 Average sleep duration < 3h or > 13h 6 Sleep duration on free days < 3h 18 Sleep duration on work days < 3h or > 13h 55 Age and sex corrected sleep duration on free days > 13h 20 Final sample size 64,110

9 Table S3. Number of Entries Per Year in the Final (Cleaned) Database, Related to Figure , , , , , , , ,695

10 Table S4. Parameters of the Polynomial Fits (y = a + bx + cx 2 + dx 3 + ex 4 + fx 5 + gx 6 ), Related to Figure 2 cat a b c d e f g R 30 y SJL M M> E E E F F> E E E TP E E E TP> E E E E SD w M M> E E E E F F> E E E TP TP> E E E SD f M E E E F E E E TP E E E ØSD M M> E E E F F> E E E TP TP> E E E BMI M E F E TP E E Abbreviations: cat = category (e.g., M 17, males younger than 18); M = males; F = females; TP = total population; SJL = social jetlag; SDw = sleep duration on workdays; SDf = sleep duration on free days; ØSD = average sleep duration over the work week; BMI = body mass index; R = regression coefficient of the fit; 30y = fit result for the age of 30 in the total population (serving as the basis for the normalisation).

11 Evaluation of the MCTQ The basic evaluation of the MCTQ comprises many different, interrelated variables. In the MCTQshift, these variables are calculated separately for each shift (e.g., morning, evening and night shift). Asterisks indicate variables that are computed from the direct answers in the MCTQ. 1. Basic Variables BTw Local time of going to bed on work days; SPrepw Local time of preparing to sleep on workdays; SLatw Sleep latency on workdays I need min to fall asleep ; * SOw Sleep onset on workdays = SPrepw + SLatw; SEw Sleep end on workdays; SIw Sleep inertia on workdays after min, I get up * GUw Local time of getting out of bed on workdays = SEw + SIw; * SDw Sleep duration on workdays = SEw SOw; * TBTw total time in bed on workdays = GUw BTw; BTf Local time of going to bed on free days; SPrepf Local time of preparing to sleep on free days; SLatf Sleep latency on free days I need min to fall asleep ; * SOf Sleep onset on free days = SPrepf + SLatf; SEf Sleep end on free days; SIf Sleep inertia on free days after min, I get up * GUf Local time of getting out of bed on free days; * SDf Sleep duration on free days = SEf SOf; * TBTf total time in bed on free days = GUf BTf; WD = number of workdays; * SDweek (Average SD across the week) = (SDw x WD + SDf x (7-WD))/7; * SLOSSweek (Sleep loss across the week): if SDweek > SDw: SLOSSweek = (SDweek SDw) x WD; if SDweek SDw: SLOSSweek = (SDweek SDf) x (7 WD); 2. Chronotype The basis for estimating chronotype is the Mid-Sleep Time on Free days (MSF) MSF = SOf + (SDf)/2; MSF is then corrected for oversleep on free days that subjects use to compensates the sleep debt accumulated during the workweek: MSFsc = MSF - (SDf - SDw)/2; This correction is only applied to individuals who sleep longer on free days than on workdays. For all others: MSFsc = MSF; MSFsc is the basic assessment for chronotype for an individual under the current circumstances; (MSFsc depends on developmental and environmental conditions, e.g., age and light exposure). For epidemiological and genetic studies, MSFsc is normalised for age and sex to make populations of different age and sex compositions comparable. 3. Social Jetlag The relative social jetlag (SJLrel) is the difference between the Mid-Sleep on work- and on free days: MSW = SOw + (SDw)/2; SJLrel = MSF MSW; The absolute social jetlag (SJL) is used for most assessments of the consequences of social jetlag: SJL = abs(sjlrel);