Sleep deprivation in teenage students is pervasive and a public health concern, but evidence is accumulating that delaying school start times may be an effective countermeasure. Most studies so far assessed static changes in schools start time, using cross-sectional comparisons and one-off sleep measures. When a high school in Germany introduced flexible start times for their senior students—allowing them to choose daily between an 8 am or 9 am start (≥08:50)—we monitored students’ sleep longitudinally using subjective and objective measures. Students (10–12th grade, 14–19 y) were followed 3 weeks prior and 6 weeks into the flexible system via daily sleep diaries (n = 65) and a subcohort via continuous wrist-actimetry (n = 37). Satisfaction and perceived cognitive outcomes were surveyed at study end. Comparisons between 8 am and ≥9 am-starts within the flexible system demonstrated that students slept 1.1 h longer when starting school later—independent of gender, grade, chronotype, and frequency of later starts; sleep offsets were delayed but, importantly, onsets remained unchanged. Sleep quality was increased and alarm-driven waking reduced. However, overall sleep duration in the flexible system was not extended compared to baseline—likely because students did not start later frequently enough. Nonetheless, students were highly satisfied with the flexible system and reported cognitive and sleep improvements. Therefore, flexible systems may present a viable alternative for implementing later school starts to improve teenage sleep if students can be encouraged to use the late-option frequently enough. Flexibility may increase acceptance of school start changes and speculatively even prevent delays in sleep onsets through occasional early starts.

Early school times fundamentally clash with the late sleep of teenagers. This mismatch results in chronic sleep deprivation, which poses acute and long-term health risks and impairs students' learning. Despite conclusive evidence that delaying school times has immediate benefits for sleep, the long-term effects on sleep are unresolved due to a shortage of longitudinal data. Here, we studied whether a flexible school start system, with the daily choice of an 8AM or 08:50AM-start, allowed secondary school students to improve their sleep and psychological functioning in a longitudinal pre-post design over exactly 1 year. Based on 2 waves, each with 6-9 weeks of daily sleep diary, we found that students maintained their 1-hour-sleep gain on days with later starts, both longitudinally (n=28) and cross-sectionally (n=79). This sleep gain was independent of chronotype and frequency of later starts but differed between genders. Girls were more successful in keeping early sleep onsets despite later sleep offsets, whereas boys delayed their onsets and thus had reduced sleep gains after 1 year. Students also reported psychological benefits (n=93), increased sleep quality and reduced alarm- driven waking on later school days. Despite these benefits on later schooldays, overall sleep duration was not extended in the flexible system. This was likely due to the persistently low uptake of the late- start option. If uptake can be further promoted, the flexible system is an appealing alternative to a fixed delay of school starts owing to possible circadian advantages (speculatively through prevention of phase-delays) and psychological mechanisms (e.g. sense of control).

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.

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.

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.