Sleep is essential for optimal health. The American Academy of Sleep Medicine (AASM) and Sleep Research Society (SRS) developed a consensus recommendation for the amount of sleep needed to promote optimal health in adults, using a modified RAND Appropriateness Method process. The recommendation is summarized here. A manuscript detailing the conference proceedings and evidence supporting the final recommendation statement will be published in SLEEP and the Journal of Clinical Sleep Medicine.

Mental health symptoms and disorders are common among elite athletes, may have sport related manifestations within this population and impair performance. Mental health cannot be separated from physical health, as evidenced by mental health symptoms and disorders increasing the risk of physical injury and delaying subsequent recovery. There are no evidence or consensus based guidelines for diagnosis and management of mental health symptoms and disorders in elite athletes. Diagnosis must differentiate character traits particular to elite athletes from psychosocial maladaptations.Management strategies should address all contributors to mental health symptoms and consider biopsychosocial factors relevant to athletes to maximise benefit and minimise harm. Management must involve both treatment of affected individual athletes and optimising environments in which all elite athletes train and compete. To advance a more standardised, evidence based approach to mental health symptoms and disorders in elite athletes, an International Olympic Committee Consensus Work Group critically evaluated the current state of science and provided recommendations.

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.

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).

The objective of this study was to investigate the reliability and validity of the Pittsburgh Sleep Quality Index (PSQI) in a non-clinical sample consisting of younger and older adults. There has been little research validating the PSQI with respect to multinight recording as with actigraphy, and more validation is needed in samples not specifically selected for clinical disturbance. Also, the degree to which the PSQI scores may reflect depressive symptoms versus actual sleep disturbance remains unclear. One-hundred and twelve volunteers (53 younger and 59 older) were screened for their ability to perform treadmill exercises; inclusion was not based on sleep disturbance or depression. Internal homogeneity was evaluated by correlating PSQI component scores with the global score. Global and component scores were correlated with a sleep diary, actigraphy, and centers for epidemiological studies -depression scale scores to investigate criterion validity. Results showed high internal homogeneity. PSQI global score correlated appreciably with sleep diary variables and the depression scale, but not with any actigraphic sleep variables. These results suggest that the PSQI has good internal homogeneity, but may be less reflective of actual sleep parameters than a negative cognitive viewpoint or pessimistic thinking. The sleep complaints measured may often be more indicative of general dissatisfaction than of any specifically sleep-related disturbance.

The physiologic mechanisms by which the four activities of sleep, sedentary behavior, lightintensity physical activity (LIPA), and moderate-to-vigorous physical activity (MVPA) affect health are related, but these relationships have not been well explored in adults. Research studies have commonly evaluated how time spent in one activity affects health. Because one can only increase time in one activity by decreasing time in another, such studies cannot determine the extent that a health benefit is due to one activity versus due to reallocating time among the other activities. For example, interventions to improve sleep possibly also increase time spent in MVPA. If so, the overall effect of such interventions on risk of premature mortality is due to both more MVPA and better sleep. Further, the potential for interaction between activities to affect health outcomes is largely unexplored. For example, is there a threshold of MVPA minutes per day, above which adverse health effects of sedentary behavior are eliminated? This paper considers the 24-Hour Activity Cycle (24-HAC) model as a paradigm for exploring inter-relatedness of health effects of the four activities. It discusses how to measure time spent in each of the four activities, as well as the analytical and statistical challenges in analyzing data based upon the model, including the inevitable challenge of confounding among activities. The potential usefulness of this model is described by reviewing selected research findings that aided in the creation of the model and discussing future applications of the 24-HAC model.

Advancing age was not associated with increased Self-Reported Sleep Disturbance or Self-Reported Tiredness/Lack of Energy. These results suggest that the often-reported increase in sleep problems with age is a nonlinear phenomenon, mediated by factors other than physiologic aging.

Background: The American Heart Association (AHA) recently published an updated algorithm for quantifying cardiovascular health (CVH)—the "Life's Essential 8™" score. We quantified US levels of CVH using the new score. Methods: We included non-pregnant, non-institutionalized individuals ages 2 through 79 years who were free of cardiovascular disease from the National Health and Nutrition Examination Surveys in 2013-2018. For all participants, we calculated the overall CVH score (range 0 [lowest] to 100 [highest]), as well as the score for each component of diet, physical activity (PA), nicotine exposure, sleep duration, body mass index (BMI), blood lipids, blood glucose, and blood pressure (BP), using published AHA definitions. Sample weights and design were incorporated in calculating prevalence estimates and standard errors using standard survey procedures. CVH scores were assessed across strata of age, sex, race/ethnicity, family income, and depression. Results There were 23,409 participants, representing 201,728,000 adults and 74,435,000 children. The overall mean CVH score was 64.7 (95% confidence interval [CI], 63.9-65.6) among adults using all 8 metrics, and it was 65.5 (95% CI, 64.4-66.6) for the 3 metrics available (diet, PA, and BMI) among children/adolescents ages 2 through 19 years. For adults, there were significant differences in mean overall CVH scores by sex (women: 67.0 vs. men: 62.5), age (range of mean values 62.2-68.7), and racial/ethnic group (range 59.7-68.5). Mean scores were lowest for diet, PA, and BMI metrics. There were large differences in mean scores across demographic groups for diet (range 23.8-47.7), nicotine exposure (range 63.1-85.0), blood glucose (range 65.7-88.1) and BP (range 49.5-84.0). In children, diet scores were low (mean 40.6) and were progressively lower in higher age groups (from 61.1 at ages 2-5 to 28.5 at ages 12-19); large differences were also noted in mean PA (range 63.1-88.3) and BMI (range 74.4-89.4) scores by sociodemographic group. Conclusions: The new Life's Essential 8 score helps identify large group and individual differences in CVH. Overall CVH in the US population remains well below optimal levels, and there are both broad and targeted opportunities to monitor, preserve, and improve CVH across the life course in both individuals and the population.