Mean chronotype and SDw depend on school timing and age, while SJL depends only on school timing. To evaluate how school timing and age longitudinally affect chronotype during adolescence (data distribution in Supp. Fig. 2), we ran a linear mixed-effect model with chronotype (i.e. MSFsc) as the dependent variable, including school timing (morning, afternoon or evening), age (1st or 5th year) and their interaction as fixed effects, and students’ id as a random effect (Supp. Table 1, Supp. Table 2). As in our previous cross-sectional results57, we found a main effect of school timing (F2,256=29.697, P<0.0001, partial η2 = 0.188, 90% confidence interval (CI)=0.119-0.256). Morning-attending students presented earlier chronotypes than both afternoon- and evening-attending students (Fig. 1a, Supp. Table 3), suggesting that school timing affects students’ biological time, improving its alignment to the school timing where students were randomly assigned. We also found a significant main effect of age (F1,256=41.921, P<0.0001, partial η2 = 0.141, 90% CI=0.081-0.207), with earlier chronotypes in 1st year. Importantly, a significant interaction between school timing and age (F2,256=12.062, P<0.0001, partial η2 = 0.086, 90% CI=0.036-0.142) reveals that chronotype’ changes throughout adolescence are modulated by school timing. At 1st year, adolescents’ chronotype only slightly differed between school timings, but this difference gets larger by their 5th year (Fig. 1a). Consistently with our previous cross-sectional study, school timing modulates how adolescents’ chronotype changes with age.
To evaluate whether the observed modulation was sufficient to fully, or only partially, align students’ chronotype with their school schedules, we assessed the effects of age and school timing on both social jetlag (SJL) and sleep duration (SD) levels.
First, we ran a mixed effect model for SJL including school timing and age (data distribution in Supp. Fig. 3) as fixed factors and students’ id as a random effect (Supp. Table 4, Supp. Table 5). School timing significantly affects SJL (F2,256=97.691, P<0.0001, partial η2 = 0.433, 90% CI=0.360-0.496), but we did not find significant effects neither for age (F2,256=0.194, P=0.660, partial η2 = 0.001, 90% CI=0.000-0.016) nor for the interaction between age and school timing (F2,256=2.288, P=0.104, partial η2 = 0.018, 90% CI=0.000-0.048)=. Particularly, morning-attending adolescents present significantly higher SJL levels (close to 3.5h) than their peers attending later school schedules (Fig. 1b, Supp. Table 6). In addition, afternoon-attending students present higher SJL levels than their evening-attending peers (2.18h vs. 1.74h), suggesting that afternoon school schedules also exert pressure on adolescents’ sleep timing.
Second, we ran a mixed effect model for Sleep Duration (SD) including school timing, age and type of day (weekday or free day) (data distribution in Supp. Fig. 4 and sleep timings in Supp. Table 7) as fixed effects and students’ id as a random effect (Supp. Table 8, Supp. Table 9). We found significant main effects for school timing (F2,256=32.059, P<0.0001, partial η2 = 0.200, 90% CI=0.130-0.313), age (F2,768=35.032, P<0.0001, partial η2 = 0.044, 90% CI=0.023-0.070) and type of day (F2,768=392.264, P<0.0001, partial η2 = 0.338, 90% CI=0.276-0.379). This last effect indicates that students sleep less on weekdays, as expected. Additionally the interactions between type of day and age (F2,768=10.117, P=0.002, partial η2 = 0.013, 90% CI=0.003-0.029) or school timing (F2,768=90.161, P<0.0001, partial η2 = 0.190, 90% CI=0.150-0.230) were also significant. Conversely, the interaction between age and school timing (F2,768=1.998, P=0.136, partial η2 = 0.005, 90% CI=0.000-0.015) and the triple interaction between age, school timing and type of day (F2,768=1.368, P=0.255, partial η2 = 0.004, 90% CI=0.000-0.012) were not significant. On weekdays, students sleep less when they are older and morning-attending students sleep less than adolescents with later school schedules (Fig. 1c). Instead, students do not differ in their sleep duration, despite their age or school schedule, on free days (Fig. 1c, Supp. Table 10).
Thus, morning-attending students present very short SDw (i.e. high levels of sleep loss) in their 1st year of school and this situation aggravates as adolescence progresses. The difference in SDw between school timings was not compensated by napping (Supp. Fig. 5, Supp. Table 11, Supp. Table 12, Supp. Table 13): even considering naps, morning-attending students do not reach the recommended 8h of sleep69–71. Altogether, the results presented here support that school timing modulates only partially adolescents’ chronotype with both SJL and SDw levels depending on the school timing to which students were randomly assigned at the beginning of secondary school.
The developmental change on chronotype during adolescence depends on both school timing and basal chronotype. In the previous section, we showed that mean changes in chronotype depend on age and school timing. However, the association between 1st and 5th year’s chronotypes is low-to-moderate in all school timings (morning: t=3.462, p=0.001 r=0.344, 95% CI=0.149-0.514; afternoon: t=1.769, p=0.080 r=0.182, 95% CI=-0.022-0.372; evening: t=4.439, p<0.0001 r=0.461, 95% CI=0.261-0.623) (Supp. Fig. 6). Here, we propose that basal chronotype (i.e. 1st year chronotype) might explain this lack of stability. Consistently, we contrasted the four scenarios previously described (Hypothesis box, Supp. Fig. 1) to evaluate whether basal chronotype and school timing affect the developmental changes in chronotype (i.e. age-related changes in chronotype or ΔChronotype).
Basal chronotype tertiles suggest that the developmental change in chronotype depends on basal chronotype, with adolescents in the earliest tertile delaying their chronotypes the most during secondary school (Fig. 2a, Supp. results 1). We ran a linear regression model with ΔChronotype (MSFsc 5th year - MSFsc 1st year) as the dependent variable, and both school timing and basal chronotype, as predictors (Supp. Table 14, Supp. Table 15). ΔChronotype was affected by both school timing (F2,253=19.678, P<0.0001, partial η2 = 0.135, 90% CI=0.073-0.198) and basal chronotype (F1,253=160.343, P<0.0001, partial η2 = 0.388, 90% CI=0.314-0.455). ΔChronotype was smaller for later than for earlier basal chronotypes, meaning that chronotype becomes less delayed with age for later basal chronotypes (Fig. 2b). For all school timings, the slopes of the relationship between the ΔChronotype and the basal chronotype were different to zero (Morning: b=-0.711, 95% CI=-0.888 to -0.534, t=-7.909, P<0.0001; Afternoon: b=-0.823, 95% CI=-1.009 to -0.637, t=-8.717, P<0.0001; Evening: b=-0.533, 95% CI=-0.738 to -0.327, t=-5.109, P<0.0001). However, the interaction between basal chronotype and school timing was non-significant (F2,253=2.150, P=0.119, partial η2 = 0.017, 90% CI=0.000-0.037), showing that slopes did not differ between school timings. Thus, the magnitude of ΔChronotype similarly depends on the basal chronotype for the three school timings. For example, a 1st year afternoon-attending student with a basal chronotype equal to the mean for the afternoon school timing (MSFsc=06:07) would delay their chronotype by 61min by the time s/he gets to 5th year. However, a same-class peer with a basal chronotype of 07:07 (1h later) would only delay it 12min (i.e. the difference between these students’ ΔChronotype is 49min, which is the slope of the model for the afternoon).
Our results are consistent with scenario 3: both school timing and basal chronotype additively affect ΔChronotype during adolescence, with no interaction between them. Even though morning-attending students experienced, on average, a lower delay in their chronotype from 1st to 5th year (compared with their afternoon- and evening-attending peers), overall, students with earlier basal chronotypes exhibited larger delays and those with later chronotypes showed smaller delays or advances, regardless of their school timing.
Age-related changes on SJL and SDw are associated with ΔChronotype.
Later chronotypes are associated with higher levels of social jetlag (SJL) and a lower sleep duration on weekdays (SDw), especially when attending school in the morning39, 63–68. Here we explored whether the individual changes in SJL or SDw during adolescence depend on ΔChronotype and/or school timing. We ran a linear regression model with the age-related changes on SJL (i.e. ΔSJL=SJL 5th year - SJL 1st year) (Supp. Fig. 7) as the dependent variable and ΔChronotype and school timing as predictors (Supp. Table 16, Supp. Table 17). We found significant main effects of both school timing (F2,253=4.493, P=0.012, partial η2=0.034, 90% CI=0.004-0.075) and ΔChronotype (F2,253=235.795, P<0.0001, partial η2=0.482, 90% CI=0.413-0.543). In brief, the more delayed the chronotype becomes from 1st to 5th year, the bigger the change in SJL. For example, if a hypothetical afternoon-attending student exhibits a ΔChronotype equal to the mean change for their school timing (ΔChronotype=61min, e.g. from 05:00 to 06:01), their SJL will increase by just 2min. However, another student, with a 1h-larger ΔChronotype (e.g. from 05:00 to 07:01, i.e. 121min), would increase their SJL on 35min along secondary school. Importantly, the interaction between ΔChronotype and school timing was significant (F2,253=7.021, P=0.001, partial η2 = 0.053, 90% CI=0.014-0.100). The association between ΔSJL and ΔChronotype was progressively weaker the later the school timing, even though the comparison between afternoon and evening school timings was not significant (Fig. 3a, Supp. Table 18). Morning-attending students exhibit larger changes in SJL for a given ΔChronotype, compared with their afternoon- and evening- attending peers (slope comparisons: morning vs. afternoon: t=2.767, P=0.017; morning vs. evening: t=3.552, P=0.001).
Age-related changes in SDw also showed interindividual differences (Supp. Fig. 8), even though changes on mean SDw were similar when comparing school timings (Fig. 1c). We ran a linear regression model with the age-related changes in SDw (ΔSDw=SDw 5th year - SDw 1st year) as the dependent variable, and ΔChronotype and school timing as predictors (Supp. Table 19, Supp. Table 20). As expected, the main effect of school timing was non-significant (F2,253=1.433, P=0.241, partial η2 = 0.011, 90% CI=0.000-0.037), indicating that SDw change similarly in different school timings (Fig. 1c). We found a significant main effect of ΔChronotype (F1,253=8.196, P=0.0046, partial η2 = 0.031, 90% CI=0.006-0.075) and, importantly, a significant interaction between ΔChronotype and school timing (F2,253=7.852, P<0.001, partial η2 = 0.058, 90% CI=0.017-0.108), indicating that school timing modulates the effect of ΔChronotype on age-related changes in SDw. In particular, afternoon- and evening-attending students with larger delays in their chronotype throughout adolescence exhibit less shortening, or even a lengthening, of their SDw (afternoon: b=0.191, 95% CI=0.013-0.369, t=2.108, P=0.036; evening: b=0.515, 95% CI=0.278-0.756, t=4.281, P<0.0001) (Fig. 3b). To illustrate, an average afternoon-attending student (ΔChronotype=61min) would decrease their SDw by 44min, while a peer with a 1h-larger ΔChronotype (i.e. 121min) would decrease their SDw by 32min. Note that the corresponding slope is the difference between 44min and 32min, which is 12min. On the other hand, morning-attending students with the greatest delays in their chronotypes by their 5th year, showed a tendency to shorten their SDw the most, although the slope was not different from zero (b=-0.109, 95% CI=-0.310-0.093, t=-1.062, P=0.289). Despite the fact that both the slopes for afternoon- and evening-attending students did differ from zero, only evening and morning slopes significantly differ between them (morning vs. evening: t=-3.950, P<0.001) (Supp. Table 21). Even though one would expect that age-related chronotype delays in morning-attending adolescents would be strongly associated with a comparable increase in SJL and decrease in SDw3,54, 72–74, our results show that SJL increases accordingly with the chronotype delay while SDw did not decrease as much as expected.