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.

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.

SummaryPeriods 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 (MASDA). MASDA uses a moving 24-hour-threshold and correlation procedure estimating relatively consolidated periods of sleep and wake.MASDA was validated against sleep logs and polysomnography. Sleep-log validation was performed on 2 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 1 night of polysomnography. Epoch-by-epoch analyses were conducted and comparisons of sleep measures via Bland-Altman plots and correlations.Compared with sleep logs, MASDA classified sleep with a median sensitivity of 80% (IQR = 75-86%) and specificity of 91% (87-92%). Mean onset and offset times were highly correlated (r = 0.86-0.91). Compared with polysomnography, MASDA 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 nighttime polysomnographic recordings. MASDA overestimated sleep onset (~21 min) and underestimated wake after sleep onset (~26 min), while not performing systematically different from polysomnography in other sleep parameters.These results demonstrate the validity of MASDA to faithfully estimate sleep-wake patterns in field studies. With its good performance across day- and nighttime, 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.