Sleep and Sleep Disorders in High-Level Athletes: a Scoping Review

Sleep is essential for recovery and performance in elite athletes. While actigraphy-based studies revealed suboptimal sleep in athletes, information on their subjective experience of sleep is scarce. Relatively unexplored is also the extent to which athletes' sleep is adversely affected by environmental conditions and daytime behaviours, that is sleep hygiene. This study aimed to provide insight in sleep quantity, quality and its putative association with sleep hygiene. Participants were 98 elite (youth) athletes competing at the highest (inter-)national level. Sleep quantity, quality and sleep hygiene were assessed once covering a 1-month period by using established (sub)clinical questionnaires, and repeatedly during 7 consecutive days. Sleep quality was generally healthy, although 41% of all athletes could be classified as 'poor sleeper', and 12% were identified as having a sleep disorder. Daily self-monitoring revealed sleep durations of 8:11±0:45h, but elevated wake after sleep onset of 13±19min. Sleep quality, feeling refreshed, and morning vigor were moderate at best. Regarding sleep hygiene, general measures revealed irregular sleep-wake patterns, psychological strain and activating pre-sleep behaviours. At the daily level, blue-light exposure and late-evening consumption of heavy meals were frequently reported. General sleep hygiene revealed significant associations with sleep quality (0.45<r>0.50; P<0.001). Results indicate that there is ample room for optimization, specifically in onset latency and in wake after sleep onset. Subtle improvements in sleep seem possible, and optimizing sleep hygiene, such as regular sleep-wake patterns and reducing psychological strain, may facilitate this sleep upgrading process.

Frequent air travel and the condensed game schedule typical of a National Basketball Association (NBA) team during the season, often results in accompanying sleep disturbances related to sleep length, sleep quality, and sleep timing (with highly harmful impacts on health, both physical and mental). These issues are not only problematic for NBA players, but also the coaches, training staff, and management support. In this narrative review, we summarize the detrimental effects that this travel and game schedule could have on NBA team members' sleep, as well as their physical and mental health. Multiple peer-reviewed articles address the role of sleep in athletic performance and health; however, to date, the literature focused on sleep-related issues that are unique to the NBA schedule is scarce. Firstly, this review addresses the impact of the NBA schedule, outlining the number of games and the travel involved (number of flights, the timing of flights, timings of arrival at destination and hotel); we also outline a typical daily NBA travel schedule, providing the reader a glimpse of what this encompasses. Secondly, we provide a brief overview of sleep science and discuss specific applications related to the NBA. Finally, we provide comment on the unique current situation of the NBA "bubble". Based on this review, there appears to be considerable scope for further investigation of the acute and chronic effects of sleep disturbances concerning the NBA travel and game schedule. Sleep science recommendations need to inform practice, target sleep interventions, and personalized protocols designed to enhance sleep health that can be incorporated at the organizational level.

Children with overweight or obesity are more likely to experience sleep disorders, although the role of weight in pediatric insomnia treatment has not been examined. The current study examined the relationships of high body mass with pretreatment insomnia severity and global sleep problems and the potential moderating impact of weight on changes in insomnia severity following insomnia treatment. Participants included 1,133 youth ages 2-18 years clinically referred for insomnia treatment. The Pediatric Insomnia Severity Index was collected at the initial assessment and throughout treatment as part of routine clinical care. Treatment status was coded as no treatment, early termination, and completed treatment. Secondary measures of global sleep problems at the initial assessment included the Adolescent Sleep Wake Scale, Adolescent Sleep Hygiene Scale, and Children's Sleep Habits Questionnaire. Medical chart review of visits within ± 3 months of baseline was used to obtain age-adjusted and sex-adjusted body mass index Z-score. Among adolescents, regression analyses found that higher body mass index Z-score modestly predicted baseline insomnia severity (P = .021) and worse sleep hygiene (P < .001). For children, higher body mass index Z-score was modestly associated with baseline total sleep problems (P = .006) but not insomnia severity (P = .792). Across ages, body mass index Z-score predicted neither treatment status nor insomnia improvement (P > .05). Findings were similar in categorical analyses comparing patients with overweight/obesity to healthy weight. Although there is evidence that children of higher body mass present for insomnia treatment with greater sleep concerns, body mass does not predict treatment completion or insomnia improvement. Data suggest insomnia treatment is effective irrespective of weight status. Duraccio KM, Simmons DM, Beebe DW, Byars KC. Relationship of overweight and obesity to insomnia severity, sleep quality, and insomnia improvement in a clinically referred pediatric sample. J Clin Sleep Med. 2022;18(4):1083-1091.

This study examined the relationship between sleep disorders and the risk of dementia in patients with newly diagnosed type 2 diabetes. This study used the Korean Health Screening Cohort data and included 39 135 subjects aged ≥40 years with new-onset type 2 diabetes between 2004 and 2007, with follow-up throughout 2013. Sleep disorders were measured by F51(sleep disorders not due to a substance or known physiological condition) or G47(sleep disorders) under International Classification of Diseases, Tenth Revision (ICD-10) codes as a primary diagnosis, and the adjusted hazard ratio (AHR) and 95% CI of all-cause dementia, Alzheimer disease, vascular dementia, and other dementia were estimated using multivariable Cox proportional hazards regression models. In the patients with type 2 diabetes with an age range between 42 and 84 years (M = 57.8, SD = 9.5), this study identified 2059 events of dementia during an average follow-up time of 5.7 years. In patients with type 2 diabetes, subjects with sleep disorders were associated with an increased risk of all-cause dementia (AHR, 1.46; 95% CI, 1.19-1.80), Alzheimer disease (AHR, 1.39; 95% CI, 1.02-1.88), and other dementia (AHR, 1.69; 95% CI, 1.23-2.31) compared to those without sleep disorders. Men (AHR, 1.93; 95% CI, 1.42-2.62) and older adults (AHR, 1.70; 95% CI, 1.35-2.15) with sleep disorders were associated with an increased risk of dementia than their counterparts without sleep disorders among patients with type 2 diabetes. These findings suggest that sleep disorders are significantly associated with an increased risk of dementia in patients with new-onset type 2 diabetes.

Mental health clinicians should appreciate that sleep is a fundamental human behavior and that inadequate sleep has adverse medical, psychiatric, and psychosocial consequences. Sleep disturbances interact with common mental disorders; the two are mutually exacerbating, and both must be appropriately addressed to ensure optimal outcomes for our patients. Sleep is by the brain, of the brain, and for the brain.

Background: Vitamin D deficiency is associated with sleep disorders and poor sleep quality. Whether vitamin D supplementation (VDS) helps resolve these problems remains unclear. Objective: To systematically review the effect of VDS on sleep quantity, quality, and disorders, and perform a meta-analysis of available data. Methods: The reporting of this review followed the PRISMA statement. VDS human interventions studies that reported on sleep quality, quantity, or disorders were included. Medline, CINAHL, EMBASE, PsycInfo, the Cochrane Library, Clinicaltrials.gov, and the ICTRP were searched, in addition to the references of the included articles and previous relevant reviews, without language or time restrictions. Included studies were critically appraised, findings were narratively synthesized, and a meta-analysis was conducted. Furthermore, the overall certainty of the evidence was assessed. Results: A total of 19 studies were included (13 randomized controlled trials (RCTs), 1 opportunistic addition to an RCT, 4 pre–post studies, and 1 pre–post study analyzed as a case series); 3 RCTs were meta-analyses. The risk of bias was generally low. Pre–post studies showed a significant improvement in sleep quality with VDS. Similarly, the results of the meta-analysis revealed a statistically significant decrease in the Pittsburgh Sleep Quality Index with VDS compared with placebo (mean difference, −2.33 (95% CI, −3.09, −1.57); p < 0.001; I2 = 0%), with a moderate certainty of evidence. The results regarding the effect of VDS on sleep-related impairment, difficulty, and disorders, as well as sleepiness and restless legs syndrome, were not unanimous. Conclusions: VDS is promising in improving sleep quality; however, its effect on sleep quantity and disorders needs to be further investigated.

Poor sleep quality, often resulting from poor sleep hygiene, is common among medical students. Educational interventions aimed at improving sleep knowledge are beneficial for sleep quality in healthy populations. However, sleep education is often given minimal attention in medical school curriculums. The aim of the study was to explore whether a short educational intervention could improve sleep knowledge, and consequently sleep quality, among medical students. We recruited preclinical- and clinical-stage medical students during the 2017-2018 academic year. Students completed a demographic survey, the Pittsburgh Sleep Quality Index (PSQI), the Epworth Sleepiness Scale (ESS), and the Assessment of Sleep Knowledge in Medical Education (ASKME) questionnaire. Students then attended a lecture on the physiology and importance of sleep. To assess the efficacy of the intervention, questionnaires were repeated 4 months thereafter. A total of 87 students (31 preclinical) with a mean age of 25.86 years (standard deviation [SD], 3.33), 51 of whom were women, participated in the study. At baseline, students had poor sleep quality with a PSQI mean score of 5.9 (SD, 2.37), without significant sleepiness, and a mean ESS score of 8.86 (SD, 4.32). The mean ASKME scores were consistent with poor sleep knowledge at 11.87 (SD, 4.32). After the intervention, the mean ASKME results improved to 14.15 (SD, 4.5; P < .001), whereas sleep quality did not. The effect was similar in preclinical and clinical medical students. Sleep knowledge was inadequate among medical students, who also experienced poor sleep quality. A short educational intervention improved sleep knowledge but was insufficient at improving sleep quality. Further studies are needed to determine which interventions may provide benefit in both sleep knowledge and sleep quality.

Background/Objectives: Sleep is pivotal for recovery, immunity, and energy restoration; however, sleep problems exist in elite athletes. Nutrition and supplementation strategies can play both a positive and negative role in sleep quality and quantity. Elite athletes experience unique psychological and physiological demands above non-elite athletes and may require different nutrition strategies to promote sleep. Nutrient interventions and their effect on sleep in elite athletes is an emerging area, with further research warranted. Methods: Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for Scoping Reviews and Joanna Brigg’s Institute Reviewer’s Manual for Scoping Reviews were utilised to assess the available evidence on nutrition strategies used to promote sleep in elite athlete cohorts, and we tried to identify the interventions that could be best researched in the future. NUtrition QUality Evaluation Strengthening Tools (NUQUEST) was used to enhance rigour and assess risk of bias in studies. The Paper to Podium (P2P) Matrix was used to offer practitioners practical recommendations. Results: 12 studies met the inclusion criteria for nutrition interventions or exposures to promote sleep in elite athletes. The median participant group size was 19 and study designs were considered together to ascertain potential sleep promoting strategies. Kiwifruit, Tart Cherry Juice and high dairy intake, limited to females, have demonstrated the highest potential to promote sleep in elite athletes, despite limited sample sizes. A-lactalbumin, carbohydrate pre-bed, casein, tryptophan, probiotic and meeting energy demands showed varying results on sleep quality in elite athletes. Conclusions: Kiwifruit, Tart Cherry Juice and dairy consumption offer potential nutritional interventions to promote sleep in elite athletic populations, while protein-based interventions may have a ceiling effect on sleep quality when elite athletes are already consuming >2.5 g·kg−1 body mass (BM) or are already meeting their sleep duration needs.

Sleep Disorders More Common in Older Age

Diabetes mellitus with its high prevalence and related hygienic problems is one of the most important worldwide burdens that can also cause mental and psychological side effects. The disease complications, chronic progress, treatment costs and social problems are among the issues that can affect the patients’ cognitive function and cause depression and sleep disorders. Failure to diagnose and treatment of such issues causes a poor prognosis and increases the risk of death. High prevalence of sleep disorders in diabetic adults and its association with depression and glucose metabolism encouraged us to conduct a study to compare the frequency of depression and sleep disorders in type-2 diabetic patients with non-diabetics. Methodology In a prospective case-control study design, considering the inclusion and exclusion criteria, 64 diabetic patients from 22 Bahman Hospital and 86 non-diabetic individuals as the matched group, were enrolled in the study during the years 2013 to 2014. To gather the data, Pittsburg sleep quality index and Hamilton depression rating scale was used. Results Sleep quality disorders and depression are more common in diabetic patients. In diabetic patients, percentage of depression and sleep quality disorders was 78.1, 68, and in non-diabetics, it was 50% and 36%, respectively. This study showed significant relationship between sleep quality disorders and depression between the two groups of participants. No significant relationship was found regarding control blood sugar, type of treatment and the amount of time a person is exposed to the disease and incidence of depression and sleeping problems. Conclusion Regarding the high prevalence of depression and sleep disorders in diabetic patients, it is necessary to diagnose depression and sleep disorders.&#x0D;

Introduction Sleep disorders constitute a significant public health concern recognized by the World Health Organization (WHO) in the ICD (International Classification of Diseases), with notable implications for young populations. Research demonstrates that disrupted sleep patterns significantly impair mental recovery processes and emotional stability [1]. Poor sleep quality contributes to mental health deterioration through disruption of emotional regulation and neurobiological mechanisms. Inadequate sleep compromises hypothalamic-pituitary-adrenal axis function, increasing cortisol production and stress perception, potentially leading to depressive symptoms [2]. Young adults represent the population stratum with the highest smartphone and electronic device usage rates, sometimes developing behavioural dependencies. Studies show that light exposure to these devices before falling asleep significantly disrupts sleep quality [3]. Moreover, excessive smartphone use is associated with reduced cognitive performance, negatively affecting work efficiency and academic achievement [4]. This study investigates the interactions between sleep disorders, mental health, electronic device usage, and academic performance among university students. We specifically examine how sleep quality and quantity influence students' psychological functioning, with particular attention to psychological distress. Methods The Pittsburgh Sleep Quality Index (PSQI) [5], the Kessler Psychological Distress Scale (K10) [6] and the Smartphone Application-Based Addiction Scale (SABAS) [7] were used to assess sleep quality, mental distress and problematic smartphone use, respectively. Descriptive statistics were expressed as Mean (SD), for continuous variables, and as count/percentages for categorical variables. “Good sleepers” and “Poor sleepers” were compared using Chi-square test or Fisher's exact test for categorical variables, and Student's t-test or the Wilcoxon-Mann-Whitney test for continuous variables, with significance at P &lt; 0.05. Logistic regression identified independent predictors of poor sleep quality (PSQI &gt; 5). Variables with significant univariate association (p &lt; 0.05) were included in the multivariate model, with results expressed as odds ratios (OR) with 95% confidence intervals (95% CI). Results This cross-sectional study involved 208 students from the University of Palermo, with 58.7% (n=122) enrolled in medical degree programs. The average age of the sample was 22±1.99 years, and 71.6% were female. The analysis revealed that 61.54% (n=128) of students had inadequate sleep quality. Univariate analysis showed that their exam completion rate (80.1%) was lower than that one reported for good sleepers (83.5%) (p &lt; 0.05). On average, daily smartphone use was higher among poor sleepers (6.46±3.03 vs 5.57±2.22 hour/day, p &lt; 0.05), and a significant association was found between poor sleep quality and the risk of problematic smartphone use (OR=2.83, 95%CI [ 1.27-7.00], p &lt; 0.05). Furthermore, results from K10 revealed that reporting severe psychological distress was significantly associated to poor sleep quality (OR=13.25, 95%CI= [5.34-37.28], p &lt; 0.001). The multivariate analysis confirmed that higher daily smartphone usage, measured in hours, is associated with poor sleep quality (AdjOR=1.21; 95% CI [1.02-1.45]) and, notably, subjects with high probability of severe psychological distress have significantly higher likelihood of being classified as poor sleepers (AdjOR = 9.59, 95% CI = [3.57-28.82]). Discussion Our analysis revealed a strong association between psychological distress (K10 scale) and poor sleep quality among university students. Students experiencing significant psychological distress showed markedly higher likelihood of being poor sleepers, confirming bidirectional relationships between mental health and sleep, as documented in previous research. Daily smartphone uses also emerged as a significant predictor of poor sleep quality, aligning with literature on electronic devices' detrimental effects on sleep hygiene. Smartphone light emissions, particularly blue light, suppress melatonin production and disrupt circadian rhythms [8]. These findings emphasize the importance of addressing sleep health within university mental health and academic support initiatives. The strong psychological distress-sleep quality association suggests interventions targeting either aspect may benefit the other. Universities should consider implementing screening programs to identify students at risk of sleep disorders, especially those reporting psychological distress symptoms. Additionally, digital hygiene education should be incorporated into student wellness programs to mitigate electronic devices' negative impact on sleep. Conclusions The study highlights the link between psychological distress, smartphone use, and sleep quality in university students. The strong connection between mental health struggles and sleep issues underscores the need to integrate sleep health into mental health services. Universities should promote well-being and responsible technology use to enhance academic performance and overall student health.

Maternal depressive symptoms during and after pregnancy are associated with poorer sleep quantity and quality and sleep disorders in 3.5-year-old offspring

Chapter 12 Epidemiology of sleep disorders in the general population

IN THE RECENTLY PUBLISHED BOOK, SLEEP DISORDERS: THEIR IMPACT ON PUBLIC HEALTH,1 THE EDITORS (DAMIEN LEGER AND SR PANDI-PERUMAL) SEEK “TO provide a comprehensive and up-to-date coverage of specialized topics on sleep and public health” (p xii). They accomplish that goal quite nicely in an informative and interesting volume consisting of 16 chapters with authors from North America and Europe. The international flavor is a particular strength of the book, and it ensures that the data presented and the public health issues discussed are not centric to either the United States or Western Europe. Another strength of the book is that most chapters provide overviews and definitions of terminology and concepts from the sleep field and, to a lesser extent, from the public health field. In this way, the book can be useful for professionals from either field who want to understand better the intersection of the two. The potential crossover appeal of the book is perhaps its greatest asset. Sleep researchers will gain more appreciation for the public health and economic impacts of sleep, sleep loss, and sleep disorders. Public health professionals, in turn, can learn more about how sleep related issues affect the societal conditions and individual disorders they study. While the editors do not explicitly divide the book into sections, the chapters fall into six groups. First, the opening chapters provide an introduction to population based approaches to understanding sleep as well as an overview of the types of concepts and questions addressed in the remainder of the book. The next three chapters bring a developmental perspective to understanding how sleep and sleep problems affect physical and psychological health, as well as performance. The third area covered includes environmental effects on sleep quantity and quality. These two chapters are very interesting and review a set of studies not often discussed in the sleep field. However, they are light on public health implications of these data, and they contain some inconsistencies. Those issues notwithstanding, they are informative, well-written chapters. The fourth group of chapters covers occupational and legal issues related to sleep, sleepiness, and sleep disorders. The chapters on sleep and shift work and on sleepiness and accidents, are probably most prototypical of what people think about when they consider the public health impact of sleep disorders. These chapters contain valuable data on prevalence rates of sleep problems, performance levels and accident rates related to sleepiness, and solid reviews of the economic impact of these issues. There are four chapters on sleep disorders that provide a wealth of data regarding prevalence rates, economic and quality of life effects, and potential economic savings from treatments. Some of these chapters have significant overlap in content, but that is not necessarily a bad thing. Finally, there are two chapters that cover the interface of sleep and medical disorders: sleep and pain, and sleep apnea and stroke. As with the previous several chapters, these two provide information not only on the interaction of sleep with the medical conditions, but on the impact of that interaction for both the individual and society. This book is a fascinating and educational look at the intersection of sleep and public health. It provides a plethora of data that investigators will want to use to help illustrate the significance of their work in a climate where more and more emphasis is placed on translational and applicable research. The book could serve as a text in public health schools, as it provides an example of how a given area (sleep, broadly defined) can have significant and varied influences on public health. It is a book that every policy maker should read.

Evidence suggests that sleep is a vital component of physical and health well-being. However, while sleep problems are present in individuals with mental health problems such as depression, it has not been clear whether these conditions are independent or whether they might be causally related. Indeed, if sleep or sleep disorders predispose or modify onset and outcomes of mental health issues, treatment of these factors could be explored as new mental health prevention or treatment options. The aim of this review was to examine in detail the bidirectional relationship between sleep, sleep disorders, and mental and physical health and well-being. It has considered the evidence that sleep architecture disruption, occurring through both quantity, quality, and timing of sleep as well as through the presence of sleep disorders may both influence mental health and well-being as well as be disrupted by both physical and mental health conditions. Also, the review has explored the effects of sleep disruption on mental health and performance through fatigue, mood, and vigilance. The review has considered the bidirectionality between sleep, sleep disorders, and mental health to examine how these may lead to or exacerbate mental health disorders such as affective, anxiety, autism, depressive and schizophrenia disorders but also considers how these conditions can affect sleep. The review highlights that poor sleep or the presence of a sleep disorder can increase the risks from mental health conditions such as suicidality. Furthermore, mental health conditions such as anxiety and worry can cause racing or repetitive thoughts that can keep an individual awake, leading to shortened sleep. It is important that sleep and sleep disorders are considered potential modifiable factors that could improve mental health outcomes. The important interconnect between both physical and mental health and sleep, in patient evaluations, also needs to be considered as these may affect treatment pathways and patient outcomes. Further, more robust and perspective research is required to establish the triad relationship of physical, mental health, and sleep.