Early Bird or Night Owl: Insights into Dutch Students' Study Patterns using the Medical Faculty's E-learning Registrations

Phenomenon: Educational activities for students are typically arranged without consideration of their preferences or peak performance hours. Students might prefer to study at different times based on their chronotype, aiming to optimize their performance. While face-to-face activities during the academic schedule do not offer flexibility and cannot reflect students' natural learning rhythm, asynchronous e-learning facilitates studying at one's preferred time. Given their ubiquitous accessibility, students can use e-learning resources according to their individual needs and preferences. E-learning usage data hence serves as a valuable proxy for certain study behaviors, presenting research opportunities to explore students' study patterns. This retrospective study aims to investigate when and for how long undergraduate students used medical e-learning modules. Approach: We performed a cross-sectional analysis of e-learning usage at one medical faculty in the Netherlands. We used data from 562 undergraduate multimedia e-learning modules for pre-clinical students, covering various medical topics over a span of two academic years (2018/19 and 2019/20). We employed educational data mining approaches to process the data and subsequently identified patterns in access times and durations. Findings: We obtained data from 70,805 e-learning sessions with 116,569 module visits and 1,495,342 page views. On average, students used e-learning for 16.8 min daily and stopped using a module after 10.2 min, but access patterns varied widely. E-learning was used seven days a week with an hourly access pattern during business hours on weekdays. Across all other times, there was a smooth increase or decrease in e-learning usage. During the week, more students started e-learning sessions in the morning (34.5% vs. 19.1%) while fewer students started in the afternoon (42.6% vs. 50.8%) and the evening (19.4% vs. 27.0%). We identified 'early bird' and 'night owl' user groups that show distinct study patterns. Insights: This retrospective educational data mining study reveals new insights into the study patterns of a complete student cohort during and outside lecture hours. These findings underline the value of 24/7 accessible study material. In addition, our findings may serve as a guide for researchers and educationalists seeking to develop more individualized educational programs.

Association of Eating Pattern, Chronotype, and Social Jetlag: A Cross-Sectional Study Using Data Accumulated in a Japanese Food-Logging Mobile Health Application

Chronotype (morningness-eveningness) and social jetlag (SJL; discrepancy in the sleep pattern between the weekday and weekend) are related to eating behavior and health. The association between sleep behavior and the daily macro- and micronutrient eating pattern of each meal (breakfast, lunch, and dinner) have not been discussed well and need more evidence. Here, meal pattern datasets of Japanese participants aged 20-59 years were obtained as averages over 1 month from the data stored in the food-logging app "Asken". We allocated three groups for each chronotype and SJL. Multiple regression analyses revealed that morning chronotype and small SJL were associated with higher total daily intake of potassium, fiber, magnesium, phosphorus, and vitamin K. Breakfast energy intake and consumption of nutrients, including protein, lipid, carbohydrate, and minerals, were higher in the morning chronotype or small SJL. Lunch intake of potassium, cholesterol, fiber, magnesium, and vitamin K was also higher in the morning chronotype or small SJL. Dinner energy intake and nutrient intake of proteins, lipids, carbohydrates, sodium, and saturated fatty acids were lower in the morning chronotype or small SJL. The current data would help to establish a detailed reference for dietary intake which considers eating patterns over a day.

Later school start time is associated with longer school day sleep duration and less social jetlag among Norwegian high school students: Results from a large-scale, cross-sectional study

The present study explored the associations between school start time and sleep habits among older adolescents, and whether these associations depended on circadian preference. The sample comprised 4010 high school students aged 16-17 years who completed a web-based survey on habitual school start time, sleep, and health. The survey included the Munich ChronoType Questionnaire, and the short version of the Horne-Östberg Morningness-Eveningness Questionnaire. Students were categorised according to habitual school start time (before 08:00 hours, 08:00 hours, 08:15 hours, 08:30 hours or after 08:30 hours) and circadian preference (morning, intermediate or evening). Data were analysed using two-way analyses of variance (school start time × circadian preference) and linear regression analyses. Results showed an overall effect of school start time on school day sleep duration (main effect, p < 0.001), with the latest school starters having the longest, and the earliest school starters having the shortest sleep duration (7:03 hr versus 6:16 hr; Tukey HSD p < 0.001). Similarly, later school starters generally reported shorter social jetlag and later school day wake-up times than earlier starting students (both main effect p < 0.001). Circadian preference did not modify these associations (interaction effects p > 0.05). In the crude regression analysis, 15 min later school start was associated with 7.2 min more sleep (p < 0.001). School start time remained a significant predictor of school day sleep duration when adjusted for sex, parental educational level and circadian preference (p < 0.001). Results suggest that school start time is a significant predictor of school day sleep duration among adolescents.

Later school start time is associated with better academic performance, sleep-wake rhythm characteristics, and eating behavior

There are numerous studies which show that an early school start time has a negative impact on the sleep, well-being, and academic performance of students. There is not enough information on the association between school start time and eating disorders, however: the disruption of the circadian rhythm is known to be a risk factor for eating disorders. The aim of this study is to analyze the relationship between school start time (SST) and the sleep, well-being, academic performance, and eating behavior of children and adolescents. The study was conducted in April and May 2021 in two regions of Russia: the Komi Republic and Yekaterinburg. The online study involved the anonymous and voluntary participation of 6571 students in grades 6-11 (mean age: 14.5 ± 1.6 years, 60.1% female), who have morning classes. All participants were divided into three groups according to SST: 08:00 (n = 3661), 08:30 (n = 2020), and 09:00 (n = 890). Each participant of the study indicated their place of residence, SST, age, sex, height, weight, academic performance, and filled out the Munich Chronotype Questionnaire, the Zung Self-Rating Depression Scale, and the Yale Food Addiction Scale for Children. As a result of multiple regression analysis, it was shown that schoolchildren with SST of 09:00 wake up at a later time on school days (B = 0.432; ΔR² = 0.039), sleep more (B = 0.293; ΔR² = 0.004), have less pronounced social jetlag (B = -0.223; ΔR² = 0.005) and sleep loss (B = -0.292; ΔR² = 0.005), and higher academic performance (B = 0.113; ΔR² = 0.003) than schoolchildren with SST of 08:00. As a result of logistic regression analysis, it was found that the frequency of the detection of food addiction is ~30% lower in schoolchildren with SST of 09:00 (OR = 0.690; 95% CI = 0.485-0.981) than in their peers with SST of 08:00. Thus, an overly early SST in Russia has a negative impact on the sleep function, academic performance, and eating behavior of children and adolescents.

Eveningness is associated with sedentary behavior and increased 10-year risk of cardiovascular disease: the SCAPIS pilot cohort

Chronotype reflects individual preferences for timing activities throughout the day, determined by the circadian system, environment and behavior. The relationship between chronotype, physical activity, and cardiovascular health has not been established. We studied the association between chronotype, physical activity patterns, and an estimated 10-year risk of first-onset cardiovascular disease (CVD) in the Swedish CArdioPulmonary bioImage Study (SCAPIS) pilot cohort. A cross-sectional analysis was performed in a middle-aged population (n = 812, 48% male). Self-assessed chronotype was classified as extreme morning, moderate morning, intermediate, moderate evening, or extreme evening. Time spent sedentary (SED) and in moderate to vigorous physical activity (MVPA) were derived from hip accelerometer. The newly introduced Systematic COronary Risk Evaluation 2 (SCORE2) model was used to estimate CVD risk based on gender, age, smoking status, systolic blood pressure, and non-HDL cholesterol. Extreme evening chronotypes exhibited the most sedentary lifestyle and least MVPA (55.3 ± 10.2 and 5.3 ± 2.9% of wear-time, respectively), with a dose-dependent relationship between chronotype and SED/MVPA (p < 0.001 and p = 0.001, respectively). In a multivariate generalized linear regression model, extreme evening chronotype was associated with increased SCORE2 risk compared to extreme morning type independent of confounders (β = 0.45, SE = 0.21, p = 0.031). Mediation analysis indicated SED was a significant mediator of the relationship between chronotype and SCORE2. Evening chronotype is associated with unhealthier physical activity patterns and poorer cardiovascular health compared to morning chronotype. Chronotype should be considered in lifestyle counseling and primary prevention programs as a potential modifiable risk factor.

Associations between chronotype and employment status in a longitudinal study of an elderly population

Individuals with an 'evening' chronotype tend to sleep and wake later than people described to be 'morning' type if given a free choice. Since early awakening times, due to school and occupation, may be more challenging for those with evening chronotype, they are expected to be at greater risk of adverse health, occupational and educational outcomes. Our objectives are to investigate associations between chronotype and occupational, educational and health outcomes in a longitudinal cohort. We use sleep, sociodemographic and health data from The University of Manchester Longitudinal Study of Cognition in Normal Healthy Old Age, 1982 through 2010. The relationship between employment and longitudinal midsleep trajectories were estimated using linear mixed models. Associations between employment status and Cornell Medical Index, Beck Depression Inventory scores, cortisol concentrations at different times of the day stratified by chronotype were estimated using regression. The relationship between chronotype, occupational success, education, and cognition were also examined using regression methods. In older adults, compared to non-employed participants, employed participants get up 0.45 hours earlier. Evening-type employed individuals had earlier midsleep time compared to their non-employed counterparts and had abnormal longitudinal trajectories with an increasing trend as they aged. Employed individuals with evening chronotype had a higher risk of depression than employed morning-types. Moreover, employed individuals with evening chronotype had a higher cortisol concentration at 14:00 h than non-employed individuals. In addition, memory score was lower in individuals with morning chronotype, however processing speed was higher in individuals with morning chronotype compared to evening. Morning-types had a higher age when they finished full time education. Relative to evening-types, those with morning chronotype were 6.5% more likely to be in a job classed as professional or intermediate. Our findings suggest that evening-types are at a disadvantage with regards to occupational, educational and health outcomes in older adults due to their vulnerability to circadian and sleep disruption.

A Circadian Hygiene Education Initiative Covering the Pre-pandemic and Pandemic Period Resulted in Earlier Get-Up Times in Italian University Students: An Ecological Study

The aims of the present study were to obtain sleep quality and sleep timing information in a group of university students and to evaluate the effects of a circadian hygiene education initiative. All students of the University of Padova (approximately 64,000) were contacted by e-mail (major campaigns in October 2019 and October 2020) and directed to an ad hoc website for collection of demographics and sleep quality/timing information. Participants (n = 5,740) received one of two sets of circadian hygiene advice (“A regular life” or “Bright days and dark nights”). Every month, they were then asked how easy it had been to comply and provided with the advice again. At any even month from joining, they completed the sleep quality/timing questionnaires again. Information on academic performance was obtained post hoc, together with representative samples of lecture (n = 5,972) and examination (n = 1,800) timings, plus lecture attendances (n = 25,302). Fifty-two percent of students had poor sleep quality, and 82% showed signs of social jetlag. Those who joined in October 2020, after several months of lockdown and distance learning, had better sleep quality, less social jetlag, and later sleep habits. Over approximately a year, the “Bright days and dark nights” advice resulted in significantly earlier get-up times compared with the “A regular life” advice. Similarly, it also resulted in a trend toward earlier midsleep (i.e., the midpoint, expressed as clock time, between sleep onset and sleep offset) and toward a decrease in the latency between wake-up and get-up time, with no impact on sleep duration. Significant changes in most sleep quality and sleep timing variables (i.e., fewer night awakenings, less social jetlag, and delayed sleep timing during lock-down) were observed in both advice groups over approximately a year, mostly in association with pandemic-related events characterizing 2020. Early chronotype students had better academic performances compared with their later chronotype counterparts. In a multivariate model, sleep quality, chronotype and study subject (science and technology, health and medical, or social and humanities) were independent predictors of academic performance. Taken together, these results underlie the importance of designing circadian-friendly university timetables.

A 4-year longitudinal study investigating the relationship between flexible school starts and grades

The mismatch between teenagers’ late sleep phase and early school start times results in acute and chronic sleep reductions. This is not only harmful for learning but may reduce career prospects and widen social inequalities. Delaying school start times has been shown to improve sleep at least short-term but whether this translates to better achievement is unresolved. Here, we studied whether 0.5–1.5 years of exposure to a flexible school start system, with the daily choice of an 8 AM or 8:50 AM-start, allowed secondary school students (n = 63–157, 14–21 years) to improve their quarterly school grades in a 4-year longitudinal pre-post design. We investigated whether sleep, changes in sleep or frequency of later starts predicted grade improvements. Mixed model regressions with 5111–16,724 official grades as outcomes did not indicate grade improvements in the flexible system per se or with observed sleep variables nor their changes—the covariates academic quarter, discipline and grade level had a greater effect in our sample. Importantly, our finding that intermittent sleep benefits did not translate into detectable grade changes does not preclude improvements in learning and cognition in our sample. However, it highlights that grades are likely suboptimal to evaluate timetabling interventions despite their importance for future success.

Sleep improvements on days with later school starts persist after 1 year in a flexible start system

Early school times fundamentally clash with the late sleep of teenagers. This mismatch results in chronic sleep deprivation posing acute and long-term health risks and impairing students' learning. Despite immediate short-term benefits for sleep, the long-term effects of later starts remain unresolved. In a pre-post design over 1 year, we studied a unique flexible school start system, in which 10–12th grade students chose daily between an 8:00 or 8:50AM-start. Missed study time (8:00–8:50) was compensated for during gap periods or after classes. Based on 2 waves (6–9 weeks of sleep diary each), we found that students maintained their ~ 1-h-sleep gain on later days, longitudinally (n = 28) and cross-sectionally (n = 79). This gain was independent of chronotype and frequency of later starts but attenuated for boys after 1 year. Students showed persistently better sleep quality and reduced alarm-driven waking and reported psychological benefits (n = 93) like improved motivation, concentration, and study quality on later days. Nonetheless, students chose later starts only infrequently (median 2 days/week), precluding detectable sleep extensions in the flexible system overall. Reasons for not choosing late starts were the need to make up lost study time, preference for extra study time and transport issues. Whether flexible systems constitute an appealing alternative to fixed delays given possible circadian and psychological advantages warrants further investigation.

Lecture start time and sleep characteristics: Analysis of daily diaries of undergraduate students from the LoST-Sleep project

OBJECTIVES

Emerging evidence shows that later high school start times are associated with increased sleep duration; however, little is known if this extends to the university setting. This study investigated associations of first lecture start times with sleep characteristics among university students.

DESIGN

Daily diaries.

SETTING

Lincoln, UK.

PARTICIPANTS

One hundred and fifty-five undergraduate students completed 7-night sleep diaries MEASUREMENTS: Of the plausible lecture-day diaries (Monday-to-Friday, expected N = 755 days), 567 days were lecture days (M = 3.8 lecture-days per student, SD = 1.1). The Consensus Sleep Diary was used to collect sleep characteristics. Two-level multilevel mixed effect generalized linear models were employed in the analyses.

RESULTS

Seventy-five percent of first lectures occurred before noon. Students reported short sleep (M = 7.0 hours, SD = 1.9) and fewer reported highest levels of sleep quality (42.8%) and restfulness (24.8%) when first lectures started at 09:00 or 09:30 compared to 10:00 or later. Every hour delay of first lecture start time was associated with 15.1 (95% confidence interval [CI]: 9.5; 20.7) minutes increase in sleep duration and higher odds of reporting the highest levels of sleep quality and restfulness. Focusing on attended lectures starting before noon, hourly delay of first lecture start time was associated with 37.4 (95% CI: 22.0; 52.8) minutes increased sleep duration. Bedtime, sleep time, and sleep onset latency were not significantly associated with first lecture start times.

CONCLUSION

This study found that undergraduate students had longer sleep and healthier sleep quality when university first lectures started later. The earliest lecture start time that afforded sufficient sleep duration for students was 10:00.

Validation of the Munich Actimetry Sleep Detection Algorithm for estimating sleep-wake patterns from activity recordings

Periods of sleep and wakefulness can be estimated from wrist-locomotor activity recordings via algorithms that identify periods of relative activity and inactivity. Here, we evaluated the performance of our Munich Actimetry Sleep Detection Algorithm. The Munich Actimetry Sleep Detection Algorithm uses a moving 24-h threshold and correlation procedure estimating relatively consolidated periods of sleep and wake. The Munich Actimetry Sleep Detection Algorithm was validated against sleep logs and polysomnography. Sleep-log validation was performed on two field samples collected over 54 and 34 days (median) in 34 adolescents and 28 young adults. Polysomnographic validation was performed on a clinical sample of 23 individuals undergoing one night of polysomnography. Epoch-by-epoch analyses were conducted and comparisons of sleep measures carried out via Bland-Altman plots and correlations. Compared with sleep logs, the Munich Actimetry Sleep Detection Algorithm classified sleep with a median sensitivity of 80% (interquartile range [IQR] = 75%-86%) and specificity of 91% (87%-92%). Mean onset and offset times were highly correlated (r = .86-.91). Compared with polysomnography, the Munich Actimetry Sleep Detection Algorithm reached a median sensitivity of 92% (85%-100%) but low specificity of 33% (10%-98%), owing to the low frequency of wake episodes in the night-time polysomnographic recordings. The Munich Actimetry Sleep Detection Algorithm overestimated sleep onset (~21 min) and underestimated wake after sleep onset (~26 min), while not performing systematically differently from polysomnography in other sleep parameters. These results demonstrate the validity of the Munich Actimetry Sleep Detection Algorithm in faithfully estimating sleep-wake patterns in field studies. With its good performance across daytime and night-time, it enables analyses of sleep-wake patterns in long recordings performed to assess circadian and sleep regularity and is therefore an excellent objective alternative to sleep logs in field settings.