Logo Logo
Switch language to English
Böhm, Stephanie (2012): Sleep and chronotype in adolescents: a chronobiological field study. Dissertation, LMU München: Medizinische Fakultät



As it is commonly known and confirmed by several studies, adolescence is the time in life that goes along with being the latest chronotype in community. This implies that physiologically adolescents tend to go to sleep later and get up later than other age groups. At the same time adolescents are the age group who spend the highest amount of time studying (at school), in order to prepare for their later (working) life. Since the usually requested school start times in Germany around 8:00 a.m. rather meet the needs of earlier chronotypes than those of normal- and later ones, adolescents and shift workers belong to the groups with the largest sleep deficit. There is a recognition that healthy sleep (adjacent to healthy nutrition and physical exercise) states one pillar of health and wellbeing, as well as playing a role in the consolidation of memory. For this reason it appears worthwhile to aim at optimising sleep behaviour and –circumstances in adolescents. Consequently the relations between chronotype and sleep should be understood, in order to gain new insights for the conductance of health prevention programs; especially in schools. The aim was to create one building block for such research. Thus the present study aspired to finding a method of examining the sleep of adolescents “in real life” via a field study with a mainly explorative approach. In order to do so, a simple and cost-effective method was sought, to obtain hypnograms of students in a mobile sleep lab at their school. The mobile, automated and easy to use EEG “Zeo®” was elected since it appeared to be an ideal tool for meeting the requests of the present study. This device consists of a headband with three frontal electrodes and a base station that records, inter alia, the following sleep parameters: total sleep, sleep latency, time awake after falling asleep, light sleep (stage 1 and 2), deep sleep (stage 3 and 4) and REM sleep. These are interpreted automatically so that no more manual evaluation of raw EEG-data has to be performed. After hypnograms were obtained, their data were assessed in relation with the students’ chronotypes. To do so, the total duration of the respective named sleep parameters were correlated with the chronotypes. Total durations of the respective sleep phases were also correlated with each other. Prior to deducing EEGs on two consecutive nights per student, the chronotype of each participant was determined via the Munich Chronotype Questionnaire. In order to validate the obtained data and gain further insights into the individual sleeping-behaviour of participants, these were asked to fill in sleep logs for two weeks during the test-phase. The main question of this thesis was weather common chronobiological expectations about sleep timing and –phases could be replicated in the sleep-mobile-setting, using Zeo®. In opposition to the usual observance in sleep-labs, no first night effect was seen between the first- and second nights in repeated measures ANOVA. For this reason both nights were used for further analysis in this study. The first hypothesis was that later chronotypes would be observed to fall asleep later in the sleep mobile, and wake up later. This hypothesis could not be confirmed. Similarly the second hypothesis, which expected later chronotypes to be observed to spend more time overall sleeping in the sleep mobile than earlier types, because they would have to catch up on their accumulated sleep deficit throughout the week, could not be approved. Both outcomes may be influenced by the study’s set-up in which respectively four students slept in the sleep mobile at the same time. Thus there hardly was a possibility for one student to get up or go to sleep without waking up the others. No correlation was seen between chronotype and the total duration of the above named sleep parameters. Sleep onset and sleep end were compared to an MCTQ- and sleep-log-deduced 24-h-sleep window. While sleep onset, as measured by Zeo® was correlated with the calculated value, no such correlation could be shown between calculated- and measured values for sleep end. An unexpected finding in half of the hypnograms was that students were observed to have fallen asleep via a REM-phase rather than via a light sleep phase, as usual. Testing for correlations between the total durations of sleep phases, the following observations were made: • Total sleep showed a positive correlation with light sleep. • Total sleep showed a positive correlation with REM sleep. • Time awake after falling asleep showed a negative correlation with REM sleep The latter discovery was unexpected, since there is no explanation as to why wake-up phases throughout the night might lead to a decline in REM-sleep. Due to the named unexpected findings regarding REM sleep, a post-hoc hypothesis was generated. This hypothesis assumes that Zeo® tends to confound wakefulness with states of REM. Further literature research showed a high probability of this hypothesis being correct, since 1) the frontal EEG-deductions during REM-sleep are rather similar to those deducted during wakefulness, whereas alpha-waves that can be observed in relaxed, awake test-persons with closed eyes are ideally deducted in the dorsal regions of the head. 2) all studies in which Zeo’s® output is claimed to have a high correlation with classical polysomnography exist only as abstracts and have not yet been published completely. Furthermore, towards the end of the present study, oral communication with Zeo® approved that the device’s hardware could not facilitate a perfect evaluation of REM-phases. In retrospection the setting of measuring student’s sleep profiles in school on weekends within the sleep mobile was accepted well by students and can be recommended for further research. A limitation in this regard is the analysis of total sleep, sleep onset and sleep end that are being de-individualised by the collective residence of students in the sleep mobile. A further use of Zeo® for scientific purposes cannot be advised, while a repetition of the present study with classical EEGs is regarded to be commendable. Although such proceeding would include a higher workload in applying and manually evaluating EEGs, the reliability of data would be considerably higher. Moreover, the raw, unevaluated data that would be obtained could be used for a refined evaluation. In the meantime Zeo® may well serve for use in health care programs, where it could be applied for individuals to gain insights into their own sleep, its structure and importance.