Logins appear to contain circadian information, validated by known biological interactions

Data on LMS login activity from 14,894 unique students were analyzed for circadian-like patterns across 4 semesters, from fall 2014 to spring 2016 (excluding summer). There was a significant effect of time-of-day for logins across students (χ2 = 70862, p < 1 × 10−308). Daily patterns were clearly visible in individual double plotted actograms (Fig. 1A), which we transformed into daily histograms, by hour. Viewed together as a surface, with rows sorted by average phase-angle of activity (Fig. 1B), students exhibited a consistent probable-sleep window between 24:00 and 06:00–10:00, while the distribution of activity during active hours ranged from early to late, suggesting these data captured the expected range of chronotypes.

Figure 1 Student LMS logins showed signs that they contain circadian rhythm information, and social disruptions thereof. A double-plotted actogram (or raster plot) of login times from an example student across one semester (A) showed daily rhythms with an apparent sleep window between 23:00 and 06:00. Each student’s activity generated a unique hourly histogram (B, single row). When arranged by average phase of activity, a range of activity phases were apparent from early (B, top) to late (B, bottom), though all students shared an apparent sleep window in the late night and early morning. A histogram for all students on days with classes (C, red) showed crenulations that aligned to class start times (C, grey area). The same histogram for non-class days (C, blue) showed a delayed phase and no such crenulations. Consistent with the resulting supposition that non-class days are more representative of circadian rhythms, and class days more disruptive to circadian rhythms, histograms of activity on non-class days still showed a range of activity phases when sorted by average non-class day phase of activity (D), whereas the same ordering of individuals for class day activity (E) showed activity on class days to be largely during class times, and devoid of apparent chronotypes. Full size image

To explore these apparent chronotypes further, all student login events were separated within each individual into events on days for which that individual had a registered class, and events on days with no class. Because some classes did not have associated days in the digital records, the final number of students with logins on class days was 14,003, and the number of students with logins on non-class days was 13,984. Significant differences existed between the activity distributions on class and non-class days (Kolmogorov-Smirnov test, D = 0.11239, p < 2 × 10−16; Fig. 1C). On class days, the bulk of activity was in the first half of the day, with multiple peaks of activity (Fig. 1C, red line) corresponding closely to class times (Fig. 1C, grey peaks). By contrast, on non-class days the majority of activity was in the latter half of the day, lacked the class-associated crenulations, and displayed a delayed onset of activity (Fig. 1C, blue line). Given these differences, we took non-class day activity to be the more natural profile, and class days to be the SJL-imposing, constrained schedule. Consistent with this assessment, activity on non-class days showed a range of apparent chronotypes, with most activity later in the day and night (Fig. 1D), while by contrast, activity on class days, using the same order of individuals (Fig. 1E), showed no apparent chronotypes, and instead most activity was restricted to class times.

LMS logins are digital and not directly biological. To validate the hypothesis that login patterns on non-class days contain proxies for biological information, we assessed known interactions of phase with gender and age, and with expected interactions with seasonal change. Men have later sleep times than women18,23,24. Consistent with this, women (N = 5887) exhibited significantly more activity than men (N = 7890) in the evening (18:00–24:00, F = 143, p = 6 × 10−33), while men exhibited significantly increased activity relative to women at night (00:00–6:00, F = 47, p = 9 × 10−12, Fig. 2A).

Figure 2 Non-class day activity distributions changed in ways expected of human circadian rhythms. Non-class day histograms sorted by gender (A) revealed that men (red) are more likely to stay active later than women (blue), with significantly increased activity between midnight and 06:00, while women showed increased activity in the evening (18:00–24:00). Sorted by decade of life (age, B), older students had significantly advanced phases, apparent both in earlier activity onset times (06:00–12:00), and decreased activity in the evening (18:00–24:00) for each additional decade of life. Sorted by season (C), there was a significant difference between fall semesters (blue) and spring semesters (red). Both fall semesters showed a consolidation of activity in the middle of the day, whereas both spring semesters showed a broader distribution of activity into the morning and evening. *p ≤ 0.05. Full size image

Circadian phase also delays with puberty25, then advances with age26,27,28. Our cohort spans from teenagers to individuals in their 70s, with a median age of 25 yr (inner quartile range (IQR) = 9 yr). Comparison across decades of age (10–19, N = 2323; 20–29, N = 8770; 30–39, N = 2337; 40–49, N = 910; 50+, N = 509) revealed a significantly earlier phase of morning activity (06:00–12:00, F = 17.05, p = 5 × 10−14) and evening activity (18:00–24:00: F = 18.29, p = 5 × 10−15; Fig. 2B) for each additional decade of life.

Data reflecting seasonal changes to activity patterns in modern humans are sparse29. Because the data spanned two years, we assessed fall semesters (fall 2014, N = 8558; fall 2015, N = 8795) and spring semesters (spring 2015, N = 8795; spring 2016, N = 8051) for signs of recurrent seasonal change across years, on the assumption that humans would still show some seasonal pattern. Consistent with this expectation, in both years, the fall and spring showed significant differences (F = 10.6, p = 0.001; Fig. 2C) in login activity distribution. In the fall semesters, students exhibited increased activity in the middle of the day, consistent with the consolidation of activity under a shortening light phase. Conversely, in the spring semesters, student activity increased in the early and late day, consistent with the spreading of activity as the light phase lengthens. In summary, non-class days are validated as containing biological circadian information because they show expected changes with gender, age, and season.

Social jet lag is strongly correlated with academic performance

SJL manifests in a difference of activity distribution as a function of day type (e.g., class or non-class days; see18,30,31). Given that we validated non-class days as containing circadian information, we predicted that greater SJL between class and non-class days would correlate with decreased academic performance, represented by lower grade point averages (GPAs). Overall, class days tend to have advanced phases when compared to non-class days (Fig. 1C). However, there is heterogeneity in the class:non-class day phase relationships of individuals. We found students with delayed, synchronized, and advanced class day phases (i.e., median login time was later, similar to, or earlier on class vs. non-class days, respectively; Fig. 3A). A comparison of phase within individuals revealed that only 40.4% of students are synchronized within one half hour of their class day phase, while 49.2% of individuals advanced, and 10.4% delayed their phases on class days by at least a half hour.

Figure 3 Social jet lag correlated with decreased academic performance for both advances and delays. Some students delayed from average non-class days (blue) to average class days (red) (A, left), some changed phase less than half an hour, on average, between non-class and class days (A, center), and some students advanced from average non-class days to average class days (A, right) (hourly histograms from example individuals shown for each condition). If SJL is calculated by linear subtraction (B), then amplitude of SJL showed a significant negative correlation with GPA for students who advanced on class days and a non-significant trend of correlation was apparent for students who delayed on class days. If SJL is calculated on a log scale (C), then amplitude of SJL showed a significant negative correlation with GPA for students who advanced on class days, and for students who delayed on class days (for both B and C, 24 groups are used, so that if SJL were random, 1 group would appear per hour of potential SJL). Full size image

We quantified SJL as the difference between the average phase of activity on class days and non-class days. SJL is typically calculated as a linear difference (subtraction of phases; Fig. 3B). However, because biological systems often show non-linear, logistic rates of change32,33,34, we also calculated SJL by taking the log of the ratio of phases (Fig. 3C). We grouped all students into 24ths of the resulting SJL scale, under the assumption that if the data were random, then we would, on average, have one point per hour of possible shift. In both cases, the sign of regressions changed at SJL = 0, and so the correlation was calculated separately for positive and negative values of SJL. Both models found that phase advance on class days significantly correlated with decreased performance (linear: R2 = 0.87, p = 5 × 10−7; nonlinear: R2 = 0.86, p = 7 × 10−7). The nonlinear model also revealed a significant negative correlation of greater delays on class days with decreased performance (R2 = 0.71, p = 4 × 10−3), while the linear model found a trend (R2 = 0.35, p = 0.09).

Social jet lag interacts with chronotype to shape academic performance

An early chronotype student taking only early morning classes and a late chronotype student taking only evening classes might both have an SJL score of 0. However, class schedules are not evenly distributed across the day, as morning classes were found to be more common (Fig. 1C,E). We hypothesized that, overall, early chronotypes would have an advantage, but that later chronotypes would have a relative advantage in later classes, when their SJL is lowest35. To test this, we assigned chronotypes to individuals based on whether their average non-class phase of activity was greater than 1 s.d. in advance of the population median (“larks”, N = 3857, median time = 12:46), within 1 s.d. of the population median (“finches”, N = 11261, median time = 16:22), or delayed more than 1 s.d. from the population median (“owls”, N = 3419, median time = 20:20). Daily profiles of the resulting chronotypes are significantly different (χ2 = 244860, p < 2.2 × 10−16; Fig. 4A). Owls and finches have a median advance on class days (1.74 h, IQR = 1.180 h; 0.49 h, IQR = 0.995 h, respectively). Larks have a median delay (0.32 h, IQR = 0.870 h; Fig. 4B).

Figure 4 Owls had an academic disadvantage regardless of class start times. Average distribution of activity across non-class days for larks (blue), finches (purple), and owls (red) (A) revealed significant differences across the three chronotype categories. Owls experienced greater average SJL between non-class days and class days (B, red, linear SJL) than either larks (blue) or finches (purple). Owls also took a significantly higher proportion of morning classes than larks or finches (C) did. Analysis of class grades as a function of both chronotype category and class start time (D) revealed a significant disadvantage for owls across the day and a significant increase in class grade across the day for all chronotype categories. When time of day was normalized across all chronotype categories (E), then there is no longer an effect of time of day, and owls showed a significant disadvantage compared to larks and finches at all times of day. In plots D and E, the lowest bar represents the average of all morning class grades taken by individuals classified as owls. *p ≤ 0.05. Full size image

We also categorized class start times as early (before 12:00), midday (12:00 to 17:00), or evening (after 17:00). Inauspiciously, owls took the highest percentage of morning classes (χ2 = 13.82, p = 0.001; Fig. 4C). All chronotypes exhibited significant academic improvement across the day (Fig. 4D; effect of time: F = 273, p = 1 × 10−118; larks: χ2 = 153, p = 7 × 10−34; finches: χ2 = 483, p = 1 × 10−105; owls: χ2 = 150, p = 2 × 10−33), with the average evening class grade points being at least 0.27 points higher than the average morning class grade points. Larks and finches significantly outperformed owls across the day (effect of chronotype: F = 391, p = 6 × 10−170). In the morning, all chronotypes differ (χ2 = 439, p = 6 × 10−96). By midday, only owls received significantly lower grade points (χ2 = 265, p = 2 × 10−58), and this effect continued in the evening (χ2 = 114, p = 2 × 10−25). In the morning, finches showed a small but significant advantage over larks (p < 0.05), consistent with our previous finding of decreased grades in students delaying themselves on class days.

The universal improvement in academic performance with later classes could arise for non-biological reasons, as in self-selection of different student groups or faculty cohorts for later classes. To control for this possibility, the comparisons were re-run on data with all grades normalized for each time of day, blinded to chronotype (Fig. 4E). Following this normalization, time of day no longer had a significant effect (F = 1.46, p = 0.2), refuting our hypothesis that owls would improve over the day while larks would worsen. Effect of chronotype remained significant, with similar overall differences as described above: owls showed a significant academic performance deficit at all class times (χ2 = 114, p = 2 × 10−25), and larks significantly underperformed finches only in the morning (p < 0.05). Available ACT scores were also compared between larks (N = 1227), finches (N = 4070), and owls (N = 1714) as a measure of educational development and college readiness (data not shown). No significant differences were found between the three groups (p = 0.06).