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Circadian alignment of rest-activity rhythms is an essential biological process that may be vulnerable to misalignment in critically ill patients. We evaluated circadian rest-activity rhythms in critically ill patients and their association with baseline (e.g. age) and clinical (e.g. mechanical ventilation status) variables, along with intensive care unit light-dark cycles. Using wrist actigraphy, we collected 48-hr activity and light exposure data from critically ill patients in a tertiary care medical intensive care unit. We evaluated circadian rest-activity rhythms using COSINOR and non-parametric circadian rhythm analysis models, and stratified these data across baseline and clinical variables. We used linear regression to evaluate the association of circadian rest-activity and light-dark exposure rhythms. In COSINOR and non-parametric circadian rhythm analysis analyses, the 34 medical intensive care unit patients completing 48-hr actigraphy recordings exhibited mean MESOR (mean activity levels of a fitted curve) and amplitudes of 0.50 ± 0.32 and 0.20 ± 0.19 movements per 30-s epoch, with high interdaily variability. Patients who were older, mechanically ventilated, sedated, restrained and with higher organ failure scores tended to exhibit greater circadian rest-activity misalignment, with three of 34 (9%) patients exhibiting no circadian rhythmicity. Circadian light-dark exposure misalignment was observed as well and was associated with rest-activity misalignment (p=0.03). Critically ill patients in our MICU experienced profound circadian rest-activity misalignment, with mostly weak or absent rhythms, along with circadian light-dark exposure misalignment. Potentially modifiable factors contributing to rest-activity misalignment (i.e. mechanical ventilation, restraints, low daytime light levels) highlight possible targets for future improvement efforts.

Based on human and animal research, it appears that light administered in the last two hours of sleep might be particularly effective as an antidepressant and in advancing circadian rhythms. Green light might be more effective than white light. An obvious advantage of light treatment during sleep is that disturbance and time cost during waking hours might be avoided. For these reasons, we decided to explore effects of a light treatment with a green light mask used for in the last hours of sleep. Subjects were 30 adults aged 18-35 years. This protocol took take place at the subjects' homes and lasted 15 days after recruitment (3-day baseline period and 12-day intervention period). Throughout the study, participants maintained sleep and compliance diaries and wore a wrist actigraph. The study was a randomized, controlled clinical trial of a bright green light mask versus a dim red light mask. The two treatments were a bright green light and dim red light. Post-treatment interviews showed that all subjects reported some discomfort from the masks, but compliance was good overall. Symptom inventory scores did not differentiate groups, suggesting no significant differences in side effects. Mood ratings and sleep questionnaires did not differentiate groups. Sleep diaries distinguished groups, suggesting worse sleep overall in the green mask group. Actigraphy showed a significant circadian phase advance in the green light group. This suggests that the green light had a physiological effect. These findings suggest that green light masks are safe and tolerable; while the green light mask worsened perceived sleep overall, no specific factors were implicated and such complaints may simply be due to earlier awakening caused by a phase advance. Also, early morning green light presented during sleep produced a significant phase shift, suggesting that while there were no symptom benefits evidenced in this healthy sample, green light masks may be useful in clinical and research applications (e.g., mood disorders, endocrine functioning, delayed sleep phase syndrome) where circadian phase shifts have resulted in positive outcomes

New sleep technologies have become pervasive in the consumer space, and are becoming highly common in research and clinical sleep settings. The rapid, widespread use of largely unregulated and unstandardized technology has enabled the quantification of many different facets of sleep health, driving scientific discovery. As sleep scientists, it is our responsibility to inform principles and practices for proper evaluation of any new technology used in the clinical and research settings, and by consumers. A current lack of standardized methods for evaluating technology performance challenges the rigor of our scientific methods for accurate representation of the sleep health facets of interest. This special article describes the rationale and priorities of an interdisciplinary effort for rigorous, standardized, and rapid performance evaluation (previously, “validation”) of new sleep and sleep disorders related technologies of all kinds (eg, devices or algorithms), including an associated article template for a new initiative for publication in Sleep Health of empirical studies systematically evaluating the performance of new sleep technologies. A structured article type should streamline manuscript development and enable more rapid writing, review, and publication. The goal is to promote rapid and rigorous evaluation and dissemination of new sleep technology, to enhance sleep research integrity, and to standardize terminology used in Rigorous Performance Evaluation papers to prevent misinterpretation while facilitating comparisons across technologies.

Digital media use is widespread in University students, and use of digital media near bedtime has a broadly negative effect on sleep outcomes. Adequate and good quality sleep is important for physical and mental health, but few studies have rigorously measured both sleep and digital media use. In this study, we investigated whether self-reported sleep patterns were associated with digital media use in a first-year University student (N?=?254, 48% male) population. Students tracked their sleep through daily online diaries and provided digital media use data in 15-min?blocks for 2?h prior to bedtime on nine occasions. A longer duration of digital media use was associated with reduced total sleep time and later bedtime, while greater diversity of digital media use was associated with increased total sleep time and earlier bedtime. Analysis of activities in the last hour before bedtime indicated that activity type plays a role in digital media's effect on sleep, with computer work, surfing the Internet, and listening to music showing the strongest relationship to multiple sleep variables. These findings have implications for physical and mental health of University students and can inform design of devices to minimize negative effects of digital media on sleep. Digital media use near bedtime has a negative effect on sleep.Adequate, good-quality sleep is important for health.Longer digital media use led to reduced sleep and later bedtime.More diverse media use was associated with longer sleep durations.Computer work, Internet use, and music had significant effects on sleep.