Questionnaire-Derived Sleep Habits and Academic Achievement in First Year University Students

This study aimed to determine the effect of sleep quantity and quality via the Pittsburgh Sleep Quality Index (PSQI) on students’ academic achievement in their first year of university study. In this cross-sectional study, 193 students (102 female, 91 male, mean ± SD; age = 19.3 ± 2.9 y) from an undergraduate Health degree in New Zealand completed the PSQI four weeks prior to the end of the semester in their first year of university study. Results from three core subjects in the first semester were averaged and correlations between the PSQI and academic success were evaluated using Spearman’s rho (ρ). The group were also trichotomized using a PSQI global score of ≤5 as the threshold for “good” sleepers (n = 62, 32%), a score of 5–8 for “moderate” sleepers (n = 63, 33%) and a score ≥8 to characterize “poor” sleepers (n = 68, 35%). Overall, students averaged 7 h 37 min of self-reported sleep duration with an average bedtime of 22:55 p.m. and wake time of 8:01 a.m. There was a significant, small inverse relationship between academic performance and bedtime (p = 0.03, ρ = −0.14), with those going to bed earlier having superior academic success. The trichotomized data demonstrated no significant differences in academic performance between students with poor, moderate and good sleep quality (p = 0.92). Later bedtimes were associated with lower academic performance in a group of first year university students. However, there were no other relationships observed between academic success and self-reported sleep quality or quantity as determined by the PSQI. Enhancing awareness of the impact of sleep timing on academic success should be prioritized and strategies to improve sleep hygiene should be promoted to university students.

Player Chronotype Does Not Affect In-Game Performance during the Evening (>18:00 h) in Professional Male Basketball Players

Sport-specific skills display diurnal variation across various team sports such as badminton and tennis serving accuracy and soccer dribbling, volleying, and chipping execution. However, the effects of athlete chronotype on in-game sport-specific skill performance according to time of day across team sports is not well understood. Therefore, the aim of this study was to identify the effect of player chronotype on in-game basketball performance during evening games. Professional male basketball players (n = 11) completed a morningness–eveningness questionnaire and were categorized according to chronotype (morning-type: n = 4; neither-type: n = 6; evening-type: n = 1). Box score data from the 2019/20 season were utilized to determine individual in-game performance during evening games played after 18:00 h. Composite metrics (i.e., effective field goal percentage, offensive rating, defensive rating, and player efficiency) were used as indicators of player performance. Non-significant (p ≥ 0.21) differences were evident between M-types and N-types for most performance measures. Small to very large effects were observed in the number of rebounds favoring M-types, and three-point shots attempted and made, assists, and steals favored N-types. In-game performance appeared to not be affected by chronotype (i.e., M-type vs. N-type) in evening games among professional male basketball players. The lack of observed effect between chronotype and in-game performance suggest coaching staff may not need to consider player chronotype when developing a match strategy or assigning player roles if largely dealing with M-types and N-types. However, to ensure the greatest specificity, coaching staff may endeavor to schedule habitual training times in line with that of competition in an effort to align player circadian rhythms to games.

Influence of Geomagnetic Disturbances at Different Times of Day on Locomotor Activity in Zebrafish (Danio Rerio)

The influence of magnetic fields and natural geomagnetic storms on biological circadian rhythms are actively studied. This study reveals an impact of local natural perturbations in the geomagnetic field that occurred at different times of the day on circadian patterns of locomotor activity of zebrafish. A decrease in zebrafish swimming speed was observed during the geomagnetic disturbances before or after the fluctuations of diurnal geomagnetic variation. However, if the geomagnetic perturbations coincided with the fluctuations of diurnal geomagnetic variation, the decrease in zebrafish swimming speed was insignificant. This result suggests that the biological effects of geomagnetic disturbances may depend on synchronization with the diurnal geomagnetic variation. It implies that the previously published correlations between geomagnetic activity and medical or biological parameters could result from a disruption in circadian biorhythms.

Clocks in Dreams: Analysis of a Long Dream Series

Many dream content analytic studies focus on dream characters, animals, social interactions and so on, but they rarely analyze the frequency of everyday objects in dreams. In the present paper, the frequency and phenomenology of clock dreams in a dream series of 12,476 dreams of a single male dreamer was analyzed. The clock dreams (0.74% of all dreams) show a variety of contexts not only related to the time management of the dreamer within the dream. Interestingly, clocks that belong to the dreamer in waking life occurred very rarely in his dreams. Given that keeping time schedules and appointments in waking life is of importance to almost everyone, the low frequency of clock dreams might be explained by novelty, that is, waking-life experiences that repeat themselves regularly do not show up in dreams that often. Thus, studying everyday objects such as clocks in dreams might help refine the current models describing the continuity between waking and dreaming.

Circadian Alterations Increase with Progression in a Patient-Derived Cell Culture Model of Breast Cancer

Circadian rhythm disruption can elicit the development of various diseases, including breast cancer. While studies have used cell lines to study correlations between altered circadian rhythms and cancer, these models have different genetic backgrounds and do not mirror the changes that occur with disease development. Isogenic cell models can recapitulate changes across cancer progression. Hence, in this study, a patient-derived breast cancer model, the 21T series, was used to evaluate changes to circadian oscillations of core clock protein transcription as cells progress from normal to malignant states. Three cell lines were used: H16N2 (normal breast epithelium), 21PT (atypical ductal hyperplasia), and 21MT-1 (invasive metastatic carcinoma). The cancerous cells are both HER2+. We assessed the transcriptional profiles of two core clock proteins, BMAL1 and PER2, which represent a positive and negative component of the molecular oscillator. In the normal H16N2 cells, both genes possessed rhythmic mRNA oscillations with close to standard periods and phases. However, in the cancerous cells, consistent changes were observed: both genes had periods that deviated farther from normal and did not have an anti-phase relationship. In the future, mechanistic studies should be undertaken to determine the oncogenic changes responsible for the circadian alterations found.

GI-SleepNet: A Highly Versatile Image-Based Sleep Classification Using a Deep Learning Algorithm

Sleep-stage classification is essential for sleep research. Various automatic judgment programs, including deep learning algorithms using artificial intelligence (AI), have been developed, but have limitations with regard to data format compatibility, human interpretability, cost, and technical requirements. We developed a novel program called GI-SleepNet, generative adversarial network (GAN)-assisted image-based sleep staging for mice that is accurate, versatile, compact, and easy to use. In this program, electroencephalogram and electromyography data are first visualized as images, and then classified into three stages (wake, NREM, and REM) by a supervised image learning algorithm. To increase its accuracy, we adopted GAN and artificially generated fake REM sleep data to equalize the number of stages. This resulted in improved accuracy, and as little as one mouse’s data yielded significant accuracy. Due to its image-based nature, the program is easy to apply to data of different formats, different species of animals, and even outside sleep research. Image data can be easily understood; thus, confirmation by experts is easily obtained, even when there are prediction anomalies. As deep learning in image processing is one of the leading fields in AI, numerous algorithms are also available.

Acute Myocardial Infarction and Daylight Saving Time Transitions: Is There a Risk?

Available evidence on the risk of acute myocardial infarction (AMI) in the days after the spring daylight saving time (DST) transition suggests either a modest increase or no risk increase. Partial sleep deprivation and enhanced circadian clock misalignment have been implicated as the underlying mechanisms for increased AMI risk, probably via enhanced thrombo-inflammatory processes and activation of the sympathetic nervous system. Most of the studies, as we suggest as a perspective here, have used potentially inappropriate control periods, including the two post-transitional weeks, because adjustment after the spring DST transition lasts at least four weeks for all chronotypes and probably even beyond this period for late chronotypes. The most plausible conclusions, at the moment, for the risk of AMI after the spring DST transition are: (1) the risk is increased, (2) a relatively modest risk increase could be currently underestimated or in some studies undetected, (3) late chronotypes and/or individuals with high levels of social jetlag (a proxy for circadian clock misalignment) could be more affected by the phenomenon, and (4) underlying pathophysiological mechanisms should be further explored. As a significant part of world’s population continues to be affected by the biannual clock change, the question of increased AMI risk in the post-transitional period remains an intriguing public health issue.

Timing and Composition of Last Meal before Bedtime Affect Sleep Parameters of Night Workers

Night workers tend to eat irregularly, both in terms of meal times and composition. The disruption in energy metabolism caused by inappropriate eating habits can negatively affect the sleep quality of these individuals. The objectives of this study were to determine the interval between the last meal and bedtime and its relationship with both diurnal and nocturnal sleep parameters, as well as to evaluate the association of the adequacy of this meal with sleep parameters. The analyses were carried out for a usual sleep routine on a workday and a day off. This cross-sectional study was part of a controlled, randomized, double-blind, crossover clinical trial. The sample comprised 30 female nursing professionals who worked permanent night shifts of 12 × 36 h. Timing and composition of the last meal were obtained from food diaries, and sleep parameters were collected via actigraphy. On multiple linear regression analysis, every hour decrease in the interval between the last meal and sleep onset there was an increase of 0.39 h on diurnal sleep duration. Regarding food intake, every 1 g of fat and 1 g of carbohydrate consumed was associated with an increase in diurnal sleep onset latency of 0.13 h and 0.02 h, respectively. These findings suggest that both timing and composition of the last meal before bedtime may be potential key factors for good diurnal and nocturnal sleep among night-shift workers.

The Impact of Chronotype on the Sleep and Training Responses of Elite Female Australian Footballers

The primary aims of the present study were to examine the impact of chronotype on sleep/wake behaviour, perceived exertion, and training load among professional footballers. Thirty-six elite female professional football player’s (mean ± SD: age, 25 ± 4 y; weight, 68 ± 7 kg) sleep and training behaviours were examined for 10 consecutive nights during a pre-season period using a self-report online player-management system and wrist activity monitors. All athletes completed the Morningness-Eveningness Questionnaire (rMEQ) on the first day of data collection. Eleven participants were morning types, seventeen participants were intermediate types, and three participants were evening types. Separate linear mixed models were conducted to assess differences in sleep, perceived exertion, and training behaviours between chronotype groups. Morning types woke up earlier (wake time: 07:19 ± 01:16 vs. 07:53 ± 01:01, p = 0.04) and reported higher ratings of perceived exertion compared to intermediate types (6.7 ± 1.1 vs. 5.9 ± 1.2, p = 0.01). No differences were observed between chronotype groups for bedtime, time in bed, total sleep time, sleep efficiency, training duration, or training load. In circumstances where professional female football players are required to train at a time opposing their natural circadian preference (e.g., morning type training in the evening), their perceived exertion during training may be higher than that of players that are training at a time that aligns with their natural circadian preference (e.g., evening type training in the evening). It is important for practitioners to monitor individual trends in training variables (e.g., rating of perceived exertion, training load) with relation to athlete chronotype and training time. Future research should examine the relationship between chronotype, training time, and rating of perceived exertion across different training durations.

Pilot Sleep Behavior across Time during Ultra-Long-Range Flights

Fatigue risk to the pilot has been a deterrent for conducting direct flights longer than 12 h under normal conditions, but such flights were a necessity during the COVID-19 pandemic. Twenty (N = 20) pilots flying across five humanitarian missions between Brazil and China wore a sleep-tracking device (the Zulu watch), which has been validated for the estimation of sleep timing (sleep onset and offset), duration, efficiency, and sleep score (wake, interrupted, light, or deep Sleep) throughout the mission period. Pilots also reported sleep timing, duration, and subjective quality of their in-flight rest periods using a sleep diary. To our knowledge, this is the first report of commercial pilot sleep behavior during ultra-long-range operations under COVID-19 pandemic conditions. Moreover, these analyses provide an estimate of sleep score during in-flight sleep, which has not been reported previously in the literature.