“Sleep? Maybe Later…” A Cross-Campus Survey of University Students and Sleep Practices

Music that is used while studying and music that is used for sleep share similar musical features, genres and subgroups

Music is an integral part of daily human life, and certain types of music are often associated with certain contexts, such as specific music for sleeping or for studying. The mood-arousal hypothesis suggests that music used for studying should be uplifting to boost arousal and increase cognitive performance while previous studies suggest that music used as a sleep aid should be calm, gentle and slow to decrease arousal. In this study, we created the Study music dataset by collecting tracks from Spotify playlists with the words 'study' or 'studying' in the title or description. In comparison with a pre-existing dataset, the Sleep music dataset, we show that the music's audio features, as defined by Spotify, are highly similar. Additionally, they share most of the same genres and have similar subgroups after a k-means clustering analysis. We suggest that both sleep music and study music aim to create a pleasant but not too disturbing auditory environment, which enables one to focus on studying and to lower arousal for sleeping. Using large Spotify-based datasets, we were able to uncover similarities between music used in two different contexts one would expect to be different.

Poor sleep quality during COVID-19 pandemic restrictions associated with reduced psychological wellbeing in healthcare students

Sleep is inexorably linked to both physiological and psychological wellbeing. Restrictions imposed to control the COVID-19 pandemic likely impacted upon daily and weekly routines, which can have a negative impact on a range of factors including sleep quality, and/or quantity and general wellbeing. The aim of this study was to investigate the effect of COVID-19 related restrictions on sleep patterns and psychological wellbeing of healthcare students. A survey was delivered to healthcare students across three faculties at a single institution. Participants completed questionnaires on the effect of COVID-19 restrictions on course delivery and clinical placements, its effect on sleep-wake times, sleep quality, sleep hygiene, psychological wellbeing, their current sleep knowledge and sleep education in their current course. Using the Pittsburgh Sleep Quality Index (PSQI), over 75% of participants were found to have poor sleep quality. Changes in sleep habits and sleep behaviours during COVID-19 restrictions were associated with poorer sleep quality, and this poor sleep quality was associated with poor psychological wellbeing, particularly, motivation, stress and fatigue. Increases in negative sleep hygiene behaviours were associated with a statistically significant increase in PSQI global score. Positive emotions were positively correlated with PSQI (r = 0.22-0.24, p < .001), negative emotions were negatively correlated with PSQI (r = -0.22 - -0.31, p < .001). Also, a self-perceived lack of sleep education was identified. This study illustrates the negative association between self-reported poor sleep behaviour and poor sleep quality during COVID-19 restrictions on university student mental health and wellbeing. Additionally, there is a self-perceived lack of sleep education with little to no time spent educating students in their current degree. Hence, appropriate sleep education may improve sleep behaviours and subsequent sleep quality, which may prove to be a protective factor against poor mental health in the face of unexpected changes to routines.

Determinants of Sleep Quality: A Cross-Sectional Study in University Students

When entering the university setting, poor sleep quality is reportedly prevalent among students and has been linked to a range of adverse health outcomes, including reduced academic performance. Moreover, determinants of sleep quality are not yet fully understood. This study was designed to (1) assess the prevalence of poor sleep quality and (2) identify determinants of sleep quality in German university students. In total, 1,684 undergraduate and graduate students (50.6% female, mean age 22.87 ± 3.15 years) from multiple academic disciplines completed a cross-sectional online survey assessing socio-demographic, health, and study-related indicators and sleep quality using the Pittsburgh Sleep Quality Index (PSQI). In our sample, 820 (48.7%) met the PSQI cut-off score (>5) for poor sleep quality. Multiple regression analysis showed that older age, being a business student, lower subjective social status, poorer self-rated health, stress, exhaustion, and poor academic performance significantly predicted poor sleep quality. Our findings document a high prevalence of poor sleep quality among university students and suggest that business students, especially, might be exposed to a greater risk for poor sleep quality. Furthermore, the results of this study are valuable for academic staff to develop tailored interventions to promote healthy sleep-in university students.

The audio features of sleep music: Universal and subgroup characteristics

Throughout history, lullabies have been used to help children sleep, and today, with the increasing accessibility of recorded music, many people report listening to music as a tool to improve sleep. Nevertheless, we know very little about this common human habit. In this study, we elucidated the characteristics of music associated with sleep by extracting audio features from a large number of tracks (N = 225,626) retrieved from sleep playlists at the global streaming platform Spotify. Compared to music in general, we found that sleep music was softer and slower; it was more often instrumental (i.e. without lyrics) and played on acoustic instruments. Yet, a large amount of variation was present in sleep music, which clustered into six distinct subgroups. Strikingly, three of the subgroups included popular tracks that were faster, louder, and more energetic than average sleep music. The findings reveal previously unknown aspects of the audio features of sleep music and highlight the individual variation in the choice of music used for sleep. By using digital traces, we were able to determine the universal and subgroup characteristics of sleep music in a unique, global dataset, advancing our understanding of how humans use music to regulate their behaviour in everyday life.

Sleep Quality and Sleep Behaviors in Varsity Athletes: A Pilot Study

Sleep hygiene practices may hinder university athletes from obtaining quality sleep to support health and performance. We sought to provide a comprehensive evaluation of sleep quality and behaviors in varsity athletes using validated sleep questionnaires: the Athlete Sleep Screening Questionnaire (ASSQ) and the Athlete Sleep Behavior Questionnaire (ASBQ). Sixty-four (n = 64) athletes participated (54% female; 71% Caucasian). The mean age was 20.3 ± 1.7 years and the mean BMI was 23.3 ± 3.3. Fifty-one percent met the threshold for adequate sleep (7+ h) and 54% reported being somewhat/very satisfied with sleep quality. Global scores for ASSQ Sleep Difficulty and ASBQ sleep behaviors were significantly correlated (r = 0.31; p = 0.014) and not significantly different across age, academic year, or residence. According to the ASSQ, 11% and 24% were classified as having severe or moderate sleep problems, respectively. The ASBQ categorized 62% as having "poor" sleep behaviors. Notable sleep-influencing factors included a high frequency of emotional/cognitive processing of sport-performance issues (46.9%), frequent use of light-emitting devices before bed (90%), training after 7 pm (65%), and the use of sleep medication (19%). Half of the university athletes did not meet the thresholds for adequate sleep, and some may require a referral for clinical sleep issues. The majority of these athletes' sleep behaviors do not promote adequate sleep. The ASSQ shows utility to assess gradations in clinical sleep difficulty; the ASBQ could be used in concert with the ASSQ to discern "cognitive and physiological arousal" targets for use in educational workshops designed to promote optimal sleep hygiene in university athletes.

Psychological predictors of insomnia, anxiety and depression in university students: potential prevention targets

BACKGROUND

Perfectionism, low self-esteem and external locus of control are psychological constructs linked to insomnia, anxiety and depression. Examining how these constructs impact mental health and serve as risk factors for the development of clinically significant symptoms may help direct psychological support resources and preventative measures for university students.

AIMS

To longitudinally examine associations between the aforementioned psychological constructs and symptoms of insomnia, anxiety and depression in a large representative sample of first-year university students.

METHOD

Electronic surveys including validated measures of the predictors and outcomes were emailed to all first-year undergraduate students at entry to a major Canadian university, and followed up on at conclusion of the academic year.

RESULTS

Compared with healthy sleepers, students screening positive for insomnia had lower self-esteem, higher self-evaluative perfectionism and increased external locus of control (all P < 0.001). Self-evaluative perfectionism (standardised β = 0.13, P < 0.01), self-esteem (β = -0.30, P < 0.001) and external locus of control (β = 0.07, P = 0.02) measured at entry were significantly associated with insomnia symptoms at follow-up. Insomnia symptoms at entry were strong predictors of symptoms of depression (β = 0.15, P < 0.001) and anxiety (β = 0.16, P < 0.001) at follow-up, even after controlling for baseline symptoms of those disorders.

CONCLUSIONS

Perfectionism, low self-esteem and external locus of control may predispose the development of insomnia symptoms in university students. In turn, insomnia symptoms appear to be robust predictors for depressive and anxiety symptoms. Sleep may be an important prevention target in university students.

Exploring the Relationship Between Sleep Quality, Sleep Hygiene, and Psychological Distress in a Sample of Canadian Varsity Athletes

Student athletes balance academic, social, and athletic demands, often leading to increased levels of stress and poor sleep. This study explores the relationship between sleep quality, sleep hygiene, and psychological distress in a sample of student athletes. Ninety-four student athletes completed the six-item Kessler Psychological Distress Scale (K6), Sleep Hygiene Practice Scale, and four components from the Pittsburgh Sleep Quality Index. Age, gender, and sport were also collected. The Pittsburgh Sleep Quality Index revealed that 44.7% of student athletes received ≥6.5 hr of sleep each night; 31% of athletes showed signs of severe mental illness according to the K6. Stepwise regression predicted K6 scores with the Pittsburgh Sleep Quality Index and the Sleep Hygiene Practice Scale scores as independent variables. A significant model accounting for 26% of the variation in K6 scores emerged; sleep schedule and sleep disturbances were significant predictors. Athletic staff should highlight the importance of sleep for mental health; suggestions on how to help athletes are provided.

Predictors of Excessive Daytime Sleepiness in Medical Students: A Meta-Regression

Excessive daytime sleepiness (EDS) is highly prevalent among medical students and can have serious negative outcomes for both students and their patients. Little is known about the magnitude and predictors of EDS among medical college students. A meta-regression analysis was conducted to achieve these two targets. A systematic search was performed for English-language studies that reported the prevalence of EDS among medical students using the Epworth sleepiness scale (ESS), age, sex, sleep duration and sleep quality as predictive variables. A total of nine observational studies (K = 9, N = 2587) were included in the analyses. Meta-regression analyses were performed using mean age (years), sex (proportion of male subjects), sleep duration (hours/night) and sleep quality index score (continuous scale) as moderators for EDS—with the prevalence of EDS as an outcome variable. An interaction term of sleep duration X sleep quality was created to assess if these two variables simultaneously influenced the outcome variable. Utilizing the ESS, the pooled prevalence of EDS among medical students was 34.6% (95% Confidence Interval (CI) 18.3–50.9%). Meta-regression models of age, sex, sleep duration and sleep quality alone revealed poor predictive capabilities. Meta-regression models of sleep duration–sleep quality interaction revealed results with high statistical significance. The findings from this review contribute supporting evidence for the relationship between sleep duration and sleep quality scores (i.e., sleep duration X sleep quality score) in predicting EDS in medical students.