Individuals vary in how their circadian system synchronizes with the cyclic environment (zeitgeber). Assessing these differences in “phase of entrainment”—often referred to as chronotype—is an important procedure in laboratory experiments and epidemiological studies but is also increasingly applied in circadian medicine, both in diagnosis and therapy. While biochemical measurements (e.g., dim-light melatonin onset [DLMO]) of internal time are still the gold standard, they are laborious, expensive, and mostly rely on special conditions (e.g., dim light). Chronotype estimation in the form of questionnaires is useful in approximating the timing of an individual’s circadian clock. They are simple, inexpensive, and location independent (e.g., administrable on- and offline) and can therefore be easily administered to many individuals. The Munich ChronoType Questionnaire (MCTQ) is an established instrument to assess chronotype by asking subjects about their sleep-wake-behavior. Here we present a shortened version of the MCTQ, the µMCTQ, for use in situations in which instrument length is critical, such as in large cohort studies. The µMCTQ contains only the core chronotype module of the standard MCTQ (stdMCTQ), which was shortened and adapted from 17 to 6 essential questions, allowing for a quick assessment of chronotype and other related parameters such as social jetlag and sleep duration. µMCTQ results correspond well to the ones collected by the stdMCTQ and are externally validated by actimetry and DLMO, assessed at home (no measure of compliance). Sleep onset, midpoint of sleep, and the µMCTQ-derived marker of chronotype showed slight deviations toward earlier times in the µMCTQ when compared with the stdMCTQ (<35 min). The µMCTQ assessment of chronotype showed good test-retest reliability and correlated significantly with phase markers from actimetry and melatonin (DLMO), especially with measurements taken on work-free days. Because of its brevity, the µMCTQ represents an ideal tool to estimate individual internal time in time-critical contexts, from large cohort studies to individualized medicine.

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.

Late chronotype, which often leads to higher social jetlag (SJL), is strongly associated with the prevalence of smoking. Any circadian disruption, strain, or misalignment, results in people not being able to live according to their biological time as is described by SJL, which we will therefore use as umbrella term. We hypothesized two scenarios potentially explaining the association between smoking and SJL: (A) If smoking delays the clock, circadian phase should advance upon quitting. (B) If people smoke more to compensate the consequences of SJL, circadian phase should not change upon quitting. To distinguish between these two hypotheses, we accompanied participants of a smoking cessation program (not involving nicotine replacement products) across the cessation intervention (3 weeks prior and 6 weeks after) by monitoring their circadian behavior, sleep quality, and daytime sleepiness via questionnaires and actimetry. Our results show no effects of cessation on SJL, chronotype, sleep quality, or daytime sleepiness, thereby favoring scenario (B). Thus, smoking may be a consequence of rather than a cause for SJL. Daytime sleepiness was a significant predictor for the outcome in our model but did not improve with cessation.

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