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The relationship between adverse childhood experiences and sleep disturbances in collegiate athletes was examined.

ABSTRACTObjectivesThis real-world study aimed to characterize the impact of illness of excessive daytime sleepiness (EDS) in patients with obstructive sleep apnea (OSA) who are adherent to continuous positive airway pressure (CPAP).MethodsThis cross-sectional study surveyed participants in Evidation Health’s Achievement app (November 2020–January 2021), a mobile consumer platform that encourages users to develop healthy habits and provides incentives to participate in research. Participants were US-resident adults who self-reported a physician diagnosis of OSA and adherence to CPAP (≥4 hours/night, ≥5 nights/week) for≥6 months. The survey included the Functional Outcomes of Sleep Questionnaire-Short Version (FOSQ-10), Epworth Sleepiness Scale (ESS), and questions regarding comorbidities, CPAP use, caffeine consumption, and physical activity. EDS was defined as ESS score >10. There were more female than male participants; therefore, data were reported separately for females/males.ResultsIn total, 476 participants (female, n = 283 [59%]; mean [SD] age, 49.7 [10.8] years; obese, 74.4%) completed the survey; 209 had EDS (mean [SD] ESS, 13.8 [2.5]) and 267 did not (mean [SD] ESS, 6.3 [2.5]). Self-reported duration of CPAP use was consistent between the EDS/no EDS cohorts, with most participants using CPAP for 7 to 9 hours/night, 7 nights/week. Participants with EDS commonly reported anxiety ([EDS/no EDS] males: 31.5%/20.0%; females: 53.7%/39.5%), depression (males: 35.6%/24.2%; females: 55.9%/44.9%), and insomnia (males: 19.2%/6.7%; females: 25.7%/12.9%) and showed impairment on the FOSQ-10 ([EDS/no EDS] males: 80.8%/35.0%; females: 91.9%/53.1%). Participants with EDS reported that sleepiness ‘very often’prevented physical activity and influenced dietary choices.ConclusionEDS influences choices related to physical activity, caffeine consumption, and diet in patients who are adherent with CPAP. More research is needed to understand the association between EDS and choices of CPAP-adherent patients. Future research should explore the health-related consequences of residual EDS associated with OSA and whether they can be mitigated by improving EDS.

ObjectivesTo examine the internal consistency reliability and measurement invariance of a questionnaire battery designed to identify college student athletes at risk for mental health symptoms and disorders.MethodsCollege student athletes (N=993) completed questionnaires assessing 13 mental health domains: strain, anxiety, depression, suicide and self-harm ideation, sleep, alcohol use, drug use, eating disorders, attention deficit hyperactivity disorder (ADHD), bipolar disorder, post-traumatic stress disorder (PTSD), gambling and psychosis. Internal consistency reliability of each measure was assessed and compared between sexes as well as to previous results in elite athletes. Discriminative ability analyses were used to examine how well the cut-off score on the strain measure (Athlete Psychological Strain Questionnaire) predicted cut-offs on other screening questionnaires.ResultsStrain, anxiety, depression, suicide and self-harm ideation, ADHD, PTSD and bipolar questionnaires all had acceptable or better internal consistency reliability. Sleep, gambling and psychosis questionnaires had questionable internal consistency reliability, although approaching acceptable for certain sex by measure values. The athlete disordered eating measure (Brief Eating Disorder in Athletes Questionnaire) had poor internal consistency reliability in males and questionable internal consistency reliability in females.ConclusionsThe recommended mental health questionnaires were generally reliable for use with college student athletes. To truly determine the validity of the cut-off scores on these self-report questionnaires, future studies need to compare the questionnaires to a structured clinical interview to determine the discriminative abilities.

Commonly held beliefs about sleep unsupported by scientific evidence (ie, myths) among adolescents and their parents/caregivers may adversely influence sleep-related attitudes and behaviors among adolescents. Thus, identifying such myths with the goal of developing effective evidence-based counter-messages has the potential to improve sleep health in adolescents.

In 2010, the American Heart Association defined a novel construct of cardiovascular health to promote a paradigm shift from a focus solely on disease treatment to one inclusive of positive health promotion and preservation across the life course in populations and individuals. Extensive subsequent evidence has provided insights into strengths and limitations of the original approach to defining and quantifying cardiovascular health. In response, the American Heart Association convened a writing group to recommend enhancements and updates. The definition and quantification of each of the original metrics (Life’s Simple 7) were evaluated for responsiveness to interindividual variation and intraindividual change. New metrics were considered, and the age spectrum was expanded to include the entire life course. The foundational contexts of social determinants of health and psychological health were addressed as crucial factors in optimizing and preserving cardiovascular health. This presidential advisory introduces an enhanced approach to assessing cardiovascular health: Life’s Essential 8. The components of Life’s Essential 8 include diet (updated), physical activity, nicotine exposure (updated), sleep health (new), body mass index, blood lipids (updated), blood glucose (updated), and blood pressure. Each metric has a new scoring algorithm ranging from 0 to 100 points, allowing generation of a new composite cardiovascular health score (the unweighted average of all components) that also varies from 0 to 100 points. Methods for implementing cardiovascular health assessment and longitudinal monitoring are discussed, as are potential data sources and tools to promote widespread adoption in policy, public health, clinical, institutional, and community settings.

Sleep health is an important consideration for athletic performance. Athletes are at high risk of insufficient sleep duration, poor sleep quality, daytime sleepiness and fatigue, suboptimal sleep schedules, irregular sleep schedules, and sleep and circadian disorders. These issues likely have an impact on athletic performance via several domains. Sleep loss and/or poor sleep quality can impair muscular strength, speed, and other aspects of physical performance. Sleep issues can also increase risk of concussions and other injuries and impair recovery after injury. Cognitive performance is also impacted in several domains, including vigilance, learning and memory, decision making, and creativity.

The COVID-19 pandemic has affected the sleep and dreams of many individuals. Some have experienced improvements, while others have had more complaints. The changes to daily life such as working from home and spending more time indoors in confinement may have disturbed the circadian rhythms of some individuals. There were many reports of a shift towards a later bedtime during the pandemic, with several studies showing that in general, females experienced worse sleep than males, including more nighttime awakenings and nightmares. Increased dream and nightmare frequency during the pandemic has been shown in multiple studies. It has been postulated that because dreams are often guided by the dominant emotional state, that dreams and nightmares related to pandemic themes are a result of specific stressors related to COVID-19. Those experiencing unwanted sleep disturbances and nightmares could stand to benefit from mindfulness and relaxation practices that can ease stress and anxiety before bedtime. Striving to maintain a regular sleep schedule and enhance exposure to daylight–particularly during the first half of the day–may also be helpful.

The COVID-19 pandemic has resulted in societal-level changes to sleep and other behavioral patterns. Objective data would allow for a greater understanding of sleep-related changes at the population level. About 163,524 active Fitbit users from 6 major US cities contributed data, representing areas particularly hard-hit by the pandemic (Chicago, Houston, Los Angeles, New York, San Francisco, and Miami). Sleep variables extracted include nightly and weekly mean sleep duration and bedtime, and variability (standard deviation) of sleep duration and bedtime. Deviation from similar timeframes in 2018 and 2019 were examined, as were changes in these sleep metrics during the pandemic, relationships to changes in resting heart rate, and changes during re-opening in May and June. Overall, compared to 2019, mean sleep duration in 2020 was higher among nearly all groups, mean sleep phase shifted later for nearly all groups, and mean sleep duration and bedtime variability decreased for nearly all groups (owing to decreased weekday-weekend differences). Over the course of January to April 2020, mean sleep duration increased, mean bedtime shifted later, and mean sleep duration variability decreased. Changes in observed resting heart rate correlated positively with changes in sleep and negatively with activity levels. In later months (May and June), many of these changes started to drift back to historical norms.

We examined the most important correlates to sleep duration and efficiency from a comprehensive array of multilevel factors.

Background: Poor sleep health, including sleep deficiency and sleep disturbance, is common among employed adults in the U.S. and is associated with undesirable workplace outcomes. Adoption of workplace health promotion programs (WHPPs) is increasing, yet few programs aim to reduce fatigue or improve sleep among employees.Objective: We analyzed data from the nationally representative 2017 Centers for Disease Control Workplace Health in America poll to identify the prevalence of sleep enhancement or fatigue reduction WHPPs and the characteristics of employers that offer these programs.Method: A stratified random sample of nationally-representative worksites with ≥10 employees was generated. It comprised 2,843 worksites. Worksite representatives reported workplace characteristics, health promotion activities, and the likelihood of offering WHPPs relating to sleep enhancement or fatigue reduction. Logistic regression analyses were utilized to identify characteristics associated with offering a sleep enhancement or fatigue reduction WHPP, controlling for WHPP budget and size of the company, and contingent on worksites having a comprehensive workplace health plan.Results: Less than 1 in 10 worksites (10%) reported offering a sleep enhancement or fatigue reduction WHPP. Worksites most likely to offer a sleep-focused WHPP were those in retail, wholesale, or technology industries (OR = 2.71, 95%CI: 1.08-6.8) vs. those in the finance, information, technology industries; those with a large WHPP budget (>$500,000, OR = 6.85, 95%CI: 2.1-22.35) vs. those with no budget; and those that had visible support of WHPP initiatives from senior leadership (OR = 4.74, 95%CI: 1.91-11.75) vs. those without such support.Conclusions: Our results highlight how few worksites reported offering sleep-focused programs for their employees. Those worksites that did feature such programs, were commonly well-resourced and had senior leadership support for WHPP initiatives in general. Future research should consider working directly with leaders to expand the implementation of employee sleep enhancement and fatigue reduction WHPPs.

Insomnia is a clinically verified nicotine withdrawal symptom. As nicotine is a stimulant, it is plausible that smoking at night could disturb sleep more than smoking at earlier times of the day, but this remains empirically unclear. This study examined smoking status and its associations with insomnia severity and sleep duration while considering the potential role of smoking time.

The objective of the present study is to identify which underlying beliefs about the impact of sleep on health may motivate change in sleep behavior.

Elite athletes are particularly susceptible to sleep inadequacies, characterised by habitual short sleep (<7 hours/night) and poor sleep quality (eg, sleep fragmentation). Athletic performance is reduced by a night or more without sleep, but the influence on performance of partial sleep restriction over 1–3 nights, a more real-world scenario, remains unclear. Studies investigating sleep in athletes often suffer from inadequate experimental control, a lack of females and questions concerning the validity of the chosen sleep assessment tools. Research only scratches the surface on how sleep influences athlete health. Studies in the wider population show that habitually sleeping <7 hours/night increases susceptibility to respiratory infection. Fortunately, much is known about the salient risk factors for sleep inadequacy in athletes, enabling targeted interventions. For example, athlete sleep is influenced by sport-specific factors (relating to training, travel and competition) and non-sport factors (eg, female gender, stress and anxiety). This expert consensus culminates with a sleep toolbox for practitioners (eg, covering sleep education and screening) to mitigate these risk factors and optimise athlete sleep. A one-size-fits-all approach to athlete sleep recommendations (eg, 7–9 hours/night) is unlikely ideal for health and performance. We recommend an individualised approach that should consider the athlete’s perceived sleep needs. Research is needed into the benefits of napping and sleep extension (eg, banking sleep).

Black smokers have earlier development of lung disease as well as poorer sleep health than whites.

Lack of control over sleep may contribute to population-level sleep disturbances, yet relatively little work has explored the degree to which an individual’s sense of control over their sleep may represent an important factor.

Sleep is an important contributor to mental and physical health. Insomnia, nightmares, and other sleep disturbances are closely linked to depression, anxiety, weight gain, diabetes, and progression of cardiometabolic disease. The high comorbidity between sleep problems, obesity, and mental illness suggest that common mechanisms are at work between them. This review discusses the presence of bivariate relations between sleep, obesity, and psychopathology, as well as the limited evidence suggesting interactions among all three. While the review identifies obstructive sleep apnea, food intake, and inflammation as potential linking mechanisms, the dearth of current literature limits strong conclusions. More data is needed to evaluate the potential moderating/mediating influences between sleep, obesity, and mental health.

Short and long sleep duration are associated with poor health outcomes and are most prevalent among racial/ethnic minorities. Few studies have investigated the intersection of other sociodemographic characteristics with race/ethnicity on sleep duration prevalence.

Biological needs for sleep are met by engaging in behaviors that are largely influenced by the environment, social norms and demands, and societal influences and pressures. Insufficient sleep duration and sleep disorders such as insomnia and sleep apnea are highly prevalent in the US population. This article outlines some of these downstream factors, including cardiovascular and metabolic disease risk, neurocognitive dysfunction, and mortality, as well as societal factors such as age, sex, race/ethnicity, and socioeconomics. This review also discusses societal factors related to sleep, such as globalization, health disparities, public policy, public safety, and changing patterns of use of technology.