Study Objectives Polysomnography (PSG) scoring is labor intensive and suffers from variability in inter- and intra-rater reliability. Automated PSG scoring has the potential to reduce the human labor costs and the variability inherent to this task. Deep learning is a form of machine learning that uses neural networks to recognize data patterns by inspecting many examples rather than by following explicit programming. Methods A sleep staging classifier trained using deep learning methods scored PSG data from the Sleep Heart Health Study (SHHS). The training set was composed of 42 560 hours of PSG data from 5213 patients. To capture higher-order data, spectrograms were generated from electroencephalography, electrooculography, and electromyography data and then passed to the neural network. A holdout set of 580 PSGs not included in the training set was used to assess model accuracy and discrimination via weighted F1-score, per-stage accuracy, and Cohen’s kappa (K). Results The optimal neural network model was composed of spectrograms in the input layer feeding into convolutional neural network layers and a long short-term memory layer to achieve a weighted F1-score of 0.87 and K = 0.82. Conclusions The deep learning sleep stage classifier demonstrates excellent accuracy and agreement with expert sleep stage scoring, outperforming human agreement on sleep staging. It achieves comparable or better F1-scores, accuracy, and Cohen’s kappa compared to literature for automated sleep stage scoring of PSG epochs. Accurate automated scoring of other PSG events may eventually allow for fully automated PSG scoring.

Physician burnout is a serious and growing threat to the medical profession and may undermine efforts to maintain a sufficient physician workforce to care for the growing and aging patient population in the United States. Burnout involves a host of complex underlying associations and potential for risk. While prevalence is unknown, recent estimates of physician burnout are quite high, approaching 50% or more, with midcareer physicians at highest risk. Sleep deprivation due to shift-work schedules, high workload, long hours, sleep interruptions, and insufficient recovery sleep have been implicated in the genesis and perpetuation of burnout. Maladaptive attitudes regarding sleep and endurance also may increase the risk for sleep deprivation among attending physicians. While duty-hour restrictions have been instituted to protect sleep opportunity among trainees, virtually no such effort has been made for attending physicians who have completed their training or practicing physicians in nonacademic settings. It is the position of the American Academy of Sleep Medicine that a critical need exists to evaluate the roles of sleep disruption, sleep deprivation, and circadian misalignment in physician well-being and burnout. Such evaluation may pave the way for the development of effective countermeasures that promote healthy sleep, with the goal of reducing burnout and its negative impacts such as a shrinking physician workforce, poor physician health and functional outcomes, lower quality of care, and compromised patient safety.

The last several years have seen intense debate about the issue of transitioning between standard and daylight saving time. In the United States, the annual advance to daylight saving time in spring, and fall back to standard time in autumn, is required by law (although some exceptions are allowed under the statute). An abundance of accumulated evidence indicates that the acute transition from standard time to daylight saving time incurs significant public health and safety risks, including increased risk of adverse cardiovascular events, mood disorders, and motor vehicle crashes. Although chronic effects of remaining in daylight saving time year-round have not been well studied, daylight saving time is less aligned with human circadian biology-which, due to the impacts of the delayed natural light/dark cycle on human activity, could result in circadian misalignment, which has been associated in some studies with increased cardiovascular disease risk, metabolic syndrome and other health risks. It is, therefore, the position of the American Academy of Sleep Medicine that these seasonal time changes should be abolished in favor of a fixed, national, year-round standard time.

Significant disease burden, as objectively measured by the Effective AHI, may still exist in many patients with severe OSA in whom PAP therapy is not utilized for the entire sleep period. The WP is a reasonably accurate device to measure the Effective AHI.

Previous research has linked sleep disturbance to anxiety. However, evidence for this relation has been inconsistent, largely limited to retrospective reports that do not account for daily variability, and silent on when the association is most pronounced. Thus, the present study utilized ecological momentary assessment (EMA) to examine the effects of daily deviations in total sleep time (TST) and person-average TST on anxiety and whether these effects varied as a function of time of day in a sample of unselected adults (N = 138). Results indicate that the amount of TST on a given night, relative to personal average TST, negatively predicted anxiety, and this relation was significant in the morning and afternoon, but not evening. In contrast, person-average TST was unrelated to average anxiety. Relations between TST and anxiety did not differ across objective (e.g., actigraphy) and subjective (e.g., sleep diary) measures. Furthermore, the pattern of results remained the same when controlling for previous day’s anxiety and were not bidirectional. These findings suggest that getting less sleep than is typical for the individual predicts subsequent anxiety, and this effect is particularly strong in the morning. Average sleep duration may be less important to the experience of anxiety than deviations from that average. These findings highlight the importance of EMA to examine how and when variability in sleep confers vulnerability for anxiety symptoms.